03_state_by_state_causal_effect_of_intervention
Kenneth Lee
10/11/2020
Introduction
In this notebook, our goal is to estimate the causal effect of different state policies on mobility signal in a state-level.
We will look at a number of states that have enforced the same type of policies, both mandatory and non-mandatory, and see if the effect of the same intervention may vary from state to state. The mobility signals are from Delphi Epidata API, which include full_time_work_prop, part_time_work_prop, completely_home_prop, and median_home_dwell_time.
The policy data is from University of Washington’s State-level social distancing policies as we will use it in model building. For the definition of the policy, please refer to the codebooks.
Based on exploratory data analysis, there is a significant decrase in mobility signal during weekends, we will drop all the weekends in the data throughout this analysis.
library(ggplot2)
library (readr)
library(tidyverse)
library(dplyr)
library(covidcast)
library(lubridate)
library(ggpubr)
library(reshape2)
library(tidyr)
library(viridis)
library(gridExtra)
library(zoo)
library(cowplot)
library(gplots)
library(car)
library(nortest)
#library(MASS)
source("code/painter.r")
source("code/loader.r")
source("code/parser.r")STARTDATE <- "2019-01-01"
ENDDATE <- lubridate::today()
GEO_TYPE = "state" # state-level
GEO_VALUE = "*" # all states
EXCLUDED_AREAS = c("as","gu", "mp","vi", "pr") # excluded areas due to small sample size# Full time away home mobility
ftime <- covidcast_signal(data_source = "safegraph",
signal ="full_time_work_prop",
start_day = STARTDATE,
end_day = ENDDATE,
geo_type = GEO_TYPE,
geo_values = GEO_VALUE)
# filter out a few states
ftime <- ftime %>%
filter(!(geo_value %in% EXCLUDED_AREAS))
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
chome <- covidcast_signal(data_source = "safegraph",
signal ="completely_home_prop",
start_day = STARTDATE,
end_day = ENDDATE,
geo_type = GEO_TYPE,
geo_values = GEO_VALUE)
# filter out a few states
chome <- chome %>%
filter(!(geo_value %in% EXCLUDED_AREAS))
# The median time spent at home for all devices at this location for this time period, in minutes
mhome<- covidcast_signal(data_source = "safegraph",
signal ="median_home_dwell_time",
start_day = STARTDATE,
end_day = ENDDATE,
geo_type = GEO_TYPE,
geo_values = GEO_VALUE)
# filter out a few states
mhome <- mhome %>%
filter(!(geo_value %in% EXCLUDED_AREAS))
# Read government intervention data
policy <- load_policy()Analysis
We would like to look at policies that have been implemented by all the states: school closure, restaurant restriction, emergency declaration, and bar restriction. The full list of policies are listed below (please refer to the codebooks for detailed definitions):
EmergDec: Emergency declaration; currently includes State of Emergency, Public Health Emergency, Public Health Disaster declarations, Civil Emergency declarations, and other permutations of state‐level declarations of emergency in response to COVID‐19.SchoolClose: Formal closing of (at minimum) public K‐12 schools.BarRestrict: Restriction or limitation of bars, breweries,wineries, tasting rooms, and/or other venues where alcoholic beverages are consumed on‐premises and sales of on‐site alcohol consumption is the primary function of the venue (i.e., bars/bar areas contained within restaurants are coded within RestaurantRestrict, as are venues that may be called bars, pubs, etc. but have food licenses)GathRestrict: gathering restrictionOtherBusinessClose: Mandate to close or substantially reduce operations of any category of business that are not classified under restaurants or bars.RestaurantRestrict: Restriction or limitation of restaurants and other venues where food is consumed on‐premises. Establishments where alcohol is served and may be called a bar or like venue but have a food license are coded within the RestaurantRestrict policy category as they are viewed as operating more like a restaurant than a bar.CaseIsolation: Policy that requires individuals with confirmed coronavirus infection (via testing) or suspected infection to self‐isolate for a specified period of time, or when they no longer test positive for infection.StayAtHome: Policy instructing individuals to stay at home for all non‐essential activities. Coding a case as a stay‐at‐home order mandate requires the executive order to using phrasing indicative of a mandate (e.g., “must stay at home”); otherwise it is coded as 0 for the “Mandate” variable if it uses advisory phrasing.PublicMask: Policy that recommends or requires individuals to wear masks or other mouth and nose coverings when they are outside their places of residence in the public.Quarantine: Quarantines mandated for people entering the state, requiring a period of self‐isolation. Quarantines may be imposed on all people entering the state, out‐of‐ state residents, or travelers from a particular state or city.NEBusinessClose: Mandate to close all non‐essential businesses. Coding a case as a closure order requires the executive order to use phrasing indicative of a mandate (e.g., “non‐essential businesses are required to close”, “non‐essential businesses must cease operations by date”).TravelRestrictIntra: Restrictions on travel within the state.TravelRestrictEntry: Travel restriction mandates that limit non‐residents from entering a given state.SchoolMask: Policy that involves requiring students to wear masks or other mouth and nose coverings while at school.TravelRestrictExit: Policies which prohibit residents of a state from leaving the state.BusinessMask: Policy that involves requiring employees to wear masks or other mouth and nose coverings as part of business operations.
Then, we focus on a number of states, in which some of them enforce one of the policies as mandatory, and some of them implement the policy as a recommendation.
# We filter down to only state wide policy
policy <- policy %>%
filter(StateWide == 1)Regression Discontinuty Design
First, we look at the simplest regression discontinuty (RD) design by regressing mobility on time in different states.
School Closure
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='SchoolClose' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact school closure policy as a mandate:") ## [1] "States that enact school closure policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "Colorado" "Connecticut"
## [7] "Delaware" "District of Columbia" "Florida"
## [10] "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa"
## [16] "Kansas" "Louisiana" "Maine"
## [19] "Maryland" "Massachusetts" "Michigan"
## [22] "Minnesota" "Mississippi" "Missouri"
## [25] "Montana" "Nebraska" "Nevada"
## [28] "New Hampshire" "New Jersey" "New Mexico"
## [31] "New York" "North Carolina" "North Dakota"
## [34] "Ohio" "Oklahoma" "Oregon"
## [37] "Pennsylvania" "Rhode Island" "South Carolina"
## [40] "Texas" "Utah" "Vermont"
## [43] "Virginia" "Washington" "West Virginia"
## [46] "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='SchoolClose' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact school closure policy as a recommendation:") ## [1] "States that enact school closure policy as a recommendation:"print(nonmandate.states$StateName)## [1] "California" "Iowa" "Kentucky" "South Dakota" "Tennessee"# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of school closure")## [1] "States that have changed the mandatory status of school closure"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## [1] "Iowa"California
plotRD(ftime,
policy,
"SchoolClose",
"ca",
STARTDATE,
ENDDATE)
Tennessee
plotRD(ftime,
policy,
"SchoolClose",
"tn",
STARTDATE,
ENDDATE)
New York
plotRD(ftime,
policy,
"SchoolClose",
"ny",
STARTDATE,
ENDDATE)
Alabama
plotRD(ftime,
policy,
"SchoolClose",
"al",
STARTDATE,
ENDDATE)
All-in-one
states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"SchoolClose",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Emergency Declaration
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='EmergDec' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact emergency declaration policy as a mandate:") ## [1] "States that enact emergency declaration policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='EmergDec' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact emergency declaration policy as a recommendation:") ## [1] "States that enact emergency declaration policy as a recommendation:"print(nonmandate.states$StateName)## character(0)# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of emergency declaration")## [1] "States that have changed the mandatory status of emergency declaration"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## character(0)All-in-one
states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"EmergDec",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Bar Restriction
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='BarRestrict' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact bar restriction policy as a mandate:") ## [1] "States that enact bar restriction policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='BarRestrict' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact bar restriction policy as a recommendation:") ## [1] "States that enact bar restriction policy as a recommendation:"print(nonmandate.states$StateName)## [1] "California"# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of bar restriction")## [1] "States that have changed the mandatory status of bar restriction"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## [1] "California"states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"BarRestrict",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Restaurant Restriction
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='RestaurantRestrict' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact restaurant restriction policy as a mandate:") ## [1] "States that enact restaurant restriction policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='RestaurantRestrict' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact restaurant restriction policy as a recommendation:") ## [1] "States that enact restaurant restriction policy as a recommendation:"print(nonmandate.states$StateName)## [1] "California"# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of restaurant restriction")## [1] "States that have changed the mandatory status of restaurant restriction"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## [1] "California"states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"RestaurantRestrict",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Linear Regression
\[y = \beta_{0} + \beta_{1}t + \beta_{2}I_t + \beta_{3}tI_t\]
Full-time-work-prop
Emergency Declaration
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*EmergDec,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.026417 -0.009086 -0.000152 0.008103 0.047791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.276e-01 9.067e-02 -4.716 2.93e-06 ***
## time_value 2.632e-05 5.005e-06 5.259 1.95e-07 ***
## EmergDec -6.447e-01 2.424e-01 -2.660 0.00800 **
## time_value:EmergDec 3.451e-05 1.317e-05 2.621 0.00897 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01315 on 673 degrees of freedom
## Multiple R-squared: 0.07389, Adjusted R-squared: 0.06976
## F-statistic: 17.9 on 3 and 673 DF, p-value: 3.474e-11
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049487 -0.020126 0.003867 0.018500 0.058335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.012e+00 1.660e-01 -6.100 1.79e-09 ***
## time_value 5.917e-05 9.162e-06 6.458 2.03e-10 ***
## EmergDec -1.461e+00 4.511e-01 -3.238 0.00126 **
## time_value:EmergDec 7.734e-05 2.451e-05 3.156 0.00167 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02424 on 673 degrees of freedom
## Multiple R-squared: 0.1554, Adjusted R-squared: 0.1516
## F-statistic: 41.27 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045534 -0.020209 0.004031 0.017795 0.045962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.460e-01 1.546e-01 -5.472 6.28e-08 ***
## time_value 4.981e-05 8.534e-06 5.836 8.29e-09 ***
## EmergDec -1.078e+00 4.132e-01 -2.610 0.00926 **
## time_value:EmergDec 5.693e-05 2.245e-05 2.536 0.01145 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02243 on 673 degrees of freedom
## Multiple R-squared: 0.1272, Adjusted R-squared: 0.1233
## F-statistic: 32.69 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03639 -0.01109 0.00309 0.01114 0.04208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.390e-01 1.068e-01 -5.047 5.77e-07 ***
## time_value 3.255e-05 5.895e-06 5.521 4.82e-08 ***
## EmergDec -4.009e-01 2.854e-01 -1.405 0.161
## time_value:EmergDec 2.059e-05 1.551e-05 1.328 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01549 on 673 degrees of freedom
## Multiple R-squared: 0.1387, Adjusted R-squared: 0.1348
## F-statistic: 36.12 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034347 -0.012780 0.003605 0.011645 0.038186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.911e-01 1.093e-01 -2.664 0.00791 **
## time_value 1.889e-05 6.033e-06 3.131 0.00182 **
## EmergDec -1.737e-01 2.758e-01 -0.630 0.52911
## time_value:EmergDec 8.734e-06 1.499e-05 0.583 0.56042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01548 on 673 degrees of freedom
## Multiple R-squared: 0.05431, Adjusted R-squared: 0.05009
## F-statistic: 12.88 on 3 and 673 DF, p-value: 3.424e-08
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042039 -0.014170 0.003516 0.014331 0.048124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.070e-01 1.329e-01 -4.567 5.90e-06 ***
## time_value 3.672e-05 7.338e-06 5.005 7.16e-07 ***
## EmergDec -5.309e-01 3.553e-01 -1.494 0.136
## time_value:EmergDec 2.741e-05 1.930e-05 1.420 0.156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01928 on 673 degrees of freedom
## Multiple R-squared: 0.1342, Adjusted R-squared: 0.1304
## F-statistic: 34.78 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050564 -0.016670 0.002116 0.019567 0.072735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.913e-01 1.736e-01 -4.558 6.13e-06 ***
## time_value 4.720e-05 9.584e-06 4.925 1.06e-06 ***
## EmergDec -3.054e-01 4.602e-01 -0.664 0.507
## time_value:EmergDec 1.452e-05 2.500e-05 0.581 0.562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0251 on 673 degrees of freedom
## Multiple R-squared: 0.1778, Adjusted R-squared: 0.1741
## F-statistic: 48.51 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035570 -0.007016 0.001160 0.010169 0.046302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.779e-01 1.147e-01 -6.780 2.63e-11 ***
## time_value 4.583e-05 6.333e-06 7.236 1.26e-12 ***
## EmergDec 1.985e-01 3.066e-01 0.647 0.518
## time_value:EmergDec -1.209e-05 1.666e-05 -0.726 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01664 on 673 degrees of freedom
## Multiple R-squared: 0.1335, Adjusted R-squared: 0.1297
## F-statistic: 34.58 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043534 -0.014009 0.003783 0.014864 0.041470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.889e-01 1.389e-01 -5.678 2.03e-08 ***
## time_value 4.667e-05 7.669e-06 6.086 1.95e-09 ***
## EmergDec -1.236e-01 3.776e-01 -0.327 0.744
## time_value:EmergDec 4.915e-06 2.051e-05 0.240 0.811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02029 on 673 degrees of freedom
## Multiple R-squared: 0.1857, Adjusted R-squared: 0.182
## F-statistic: 51.15 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037730 -0.017856 0.005213 0.013438 0.041434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.608e-01 1.265e-01 -5.223 2.35e-07 ***
## time_value 3.923e-05 6.984e-06 5.617 2.84e-08 ***
## EmergDec -5.962e-01 3.326e-01 -1.792 0.0735 .
## time_value:EmergDec 3.111e-05 1.807e-05 1.721 0.0856 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01823 on 673 degrees of freedom
## Multiple R-squared: 0.1128, Adjusted R-squared: 0.1089
## F-statistic: 28.53 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042416 -0.020517 0.004818 0.016910 0.039188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.327e-01 1.458e-01 -4.339 1.65e-05 ***
## time_value 3.781e-05 8.048e-06 4.698 3.19e-06 ***
## EmergDec -1.224e+00 3.996e-01 -3.062 0.00228 **
## time_value:EmergDec 6.498e-05 2.171e-05 2.993 0.00286 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02137 on 673 degrees of freedom
## Multiple R-squared: 0.1238, Adjusted R-squared: 0.1199
## F-statistic: 31.69 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0122597 -0.0039008 0.0003853 0.0037220 0.0158008
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.565e-01 3.653e-02 -7.020 5.43e-12 ***
## time_value 1.567e-05 2.017e-06 7.769 2.96e-14 ***
## EmergDec -9.552e-03 9.221e-02 -0.104 0.918
## time_value:EmergDec 3.047e-07 5.013e-06 0.061 0.952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005174 on 673 degrees of freedom
## Multiple R-squared: 0.1104, Adjusted R-squared: 0.1065
## F-statistic: 27.85 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060198 -0.024632 0.001803 0.024630 0.080625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.365e+00 2.096e-01 -6.509 1.48e-10 ***
## time_value 7.913e-05 1.157e-05 6.837 1.81e-11 ***
## EmergDec -1.029e+00 5.511e-01 -1.867 0.0624 .
## time_value:EmergDec 5.317e-05 2.995e-05 1.775 0.0763 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03021 on 673 degrees of freedom
## Multiple R-squared: 0.1756, Adjusted R-squared: 0.1719
## F-statistic: 47.79 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040569 -0.016174 0.004098 0.013824 0.038512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.760e-01 1.213e-01 -4.748 2.52e-06 ***
## time_value 3.475e-05 6.697e-06 5.188 2.81e-07 ***
## EmergDec -7.248e-01 3.298e-01 -2.198 0.0283 *
## time_value:EmergDec 3.828e-05 1.792e-05 2.137 0.0330 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01772 on 673 degrees of freedom
## Multiple R-squared: 0.0922, Adjusted R-squared: 0.08815
## F-statistic: 22.78 on 3 and 673 DF, p-value: 4.665e-14
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051453 -0.016370 0.000582 0.016973 0.071834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.180e+00 1.761e-01 -6.699 4.42e-11 ***
## time_value 6.868e-05 9.723e-06 7.064 4.05e-12 ***
## EmergDec 3.137e-01 4.630e-01 0.678 0.498
## time_value:EmergDec -1.954e-05 2.516e-05 -0.776 0.438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02538 on 673 degrees of freedom
## Multiple R-squared: 0.2187, Adjusted R-squared: 0.2152
## F-statistic: 62.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043499 -0.017842 0.003002 0.016872 0.054311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.224e-01 1.443e-01 -5.697 1.82e-08 ***
## time_value 4.837e-05 7.970e-06 6.069 2.15e-09 ***
## EmergDec -5.349e-01 3.703e-01 -1.445 0.149
## time_value:EmergDec 2.750e-05 2.013e-05 1.366 0.172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02059 on 673 degrees of freedom
## Multiple R-squared: 0.1294, Adjusted R-squared: 0.1256
## F-statistic: 33.35 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050910 -0.019817 0.002065 0.019446 0.051069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.112e+00 1.704e-01 -6.528 1.31e-10 ***
## time_value 6.478e-05 9.405e-06 6.888 1.30e-11 ***
## EmergDec -1.148e+00 4.592e-01 -2.500 0.0126 *
## time_value:EmergDec 6.009e-05 2.495e-05 2.409 0.0163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0248 on 673 degrees of freedom
## Multiple R-squared: 0.1846, Adjusted R-squared: 0.1809
## F-statistic: 50.78 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048569 -0.018830 0.002522 0.018900 0.061169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.898e-01 1.630e-01 -6.074 2.09e-09 ***
## time_value 5.782e-05 8.998e-06 6.426 2.48e-10 ***
## EmergDec 4.220e-02 4.181e-01 0.101 0.920
## time_value:EmergDec -4.176e-06 2.272e-05 -0.184 0.854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02324 on 673 degrees of freedom
## Multiple R-squared: 0.1466, Adjusted R-squared: 0.1428
## F-statistic: 38.53 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040618 -0.017629 0.003188 0.016161 0.045429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.344e-01 1.402e-01 -6.666 5.47e-11 ***
## time_value 5.473e-05 7.737e-06 7.074 3.79e-12 ***
## EmergDec -8.596e-01 3.746e-01 -2.294 0.0221 *
## time_value:EmergDec 4.503e-05 2.036e-05 2.212 0.0273 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02033 on 673 degrees of freedom
## Multiple R-squared: 0.1473, Adjusted R-squared: 0.1435
## F-statistic: 38.74 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045636 -0.013099 0.001137 0.017257 0.060335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.225e-01 1.569e-01 -4.605 4.92e-06 ***
## time_value 4.335e-05 8.661e-06 5.005 7.15e-07 ***
## EmergDec -1.461e-01 4.159e-01 -0.351 0.725
## time_value:EmergDec 6.071e-06 2.260e-05 0.269 0.788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02268 on 673 degrees of freedom
## Multiple R-squared: 0.1768, Adjusted R-squared: 0.1731
## F-statistic: 48.17 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046822 -0.013950 0.002482 0.015651 0.059148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.287e-01 1.583e-01 -5.235 2.20e-07 ***
## time_value 4.903e-05 8.739e-06 5.610 2.95e-08 ***
## EmergDec 4.521e-01 4.028e-01 1.122 0.262
## time_value:EmergDec -2.647e-05 2.189e-05 -1.209 0.227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0225 on 673 degrees of freedom
## Multiple R-squared: 0.186, Adjusted R-squared: 0.1824
## F-statistic: 51.26 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038186 -0.015932 0.001911 0.014898 0.046994
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.336e-01 1.297e-01 -4.886 1.29e-06 ***
## time_value 3.817e-05 7.158e-06 5.332 1.32e-07 ***
## EmergDec -3.280e-01 3.584e-01 -0.915 0.360
## time_value:EmergDec 1.642e-05 1.947e-05 0.843 0.399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01907 on 673 degrees of freedom
## Multiple R-squared: 0.1281, Adjusted R-squared: 0.1242
## F-statistic: 32.96 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038241 -0.014689 0.000265 0.014968 0.059921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.022e-01 1.025e-01 -2.948 0.003305 **
## time_value 1.970e-05 5.648e-06 3.488 0.000518 ***
## EmergDec -6.970e-01 4.535e-01 -1.537 0.124845
## time_value:EmergDec 3.672e-05 2.462e-05 1.491 0.136360
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02061 on 673 degrees of freedom
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.1116
## F-statistic: 29.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053030 -0.016667 0.001861 0.018253 0.054131
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.213e+00 1.749e-01 -6.937 9.39e-12 ***
## time_value 7.058e-05 9.652e-06 7.312 7.49e-13 ***
## EmergDec 2.547e-02 4.752e-01 0.054 0.957
## time_value:EmergDec -4.052e-06 2.582e-05 -0.157 0.875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02554 on 673 degrees of freedom
## Multiple R-squared: 0.2414, Adjusted R-squared: 0.238
## F-statistic: 71.39 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051692 -0.020794 0.003311 0.020207 0.053376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.131e+00 1.764e-01 -6.408 2.77e-10 ***
## time_value 6.595e-05 9.739e-06 6.771 2.79e-11 ***
## EmergDec -1.118e+00 4.795e-01 -2.331 0.0200 *
## time_value:EmergDec 5.837e-05 2.605e-05 2.241 0.0254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02577 on 673 degrees of freedom
## Multiple R-squared: 0.1825, Adjusted R-squared: 0.1789
## F-statistic: 50.09 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040804 -0.020633 0.004182 0.018109 0.043497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.494e-01 1.509e-01 -4.966 8.68e-07 ***
## time_value 4.446e-05 8.330e-06 5.337 1.29e-07 ***
## EmergDec -1.467e+00 4.136e-01 -3.548 0.000416 ***
## time_value:EmergDec 7.818e-05 2.247e-05 3.480 0.000535 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02212 on 673 degrees of freedom
## Multiple R-squared: 0.1195, Adjusted R-squared: 0.1156
## F-statistic: 30.46 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037483 -0.014949 0.002592 0.013345 0.038015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.156e-01 1.169e-01 -5.265 1.89e-07 ***
## time_value 3.719e-05 6.454e-06 5.762 1.26e-08 ***
## EmergDec -4.518e-01 3.151e-01 -1.434 0.152
## time_value:EmergDec 2.348e-05 1.712e-05 1.371 0.171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01702 on 673 degrees of freedom
## Multiple R-squared: 0.09047, Adjusted R-squared: 0.08642
## F-statistic: 22.31 on 3 and 673 DF, p-value: 8.761e-14
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.039868 -0.017725 0.004292 0.015260 0.052927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.778e-01 1.351e-01 -4.277 2.17e-05 ***
## time_value 3.468e-05 7.458e-06 4.651 3.98e-06 ***
## EmergDec -3.723e-01 3.581e-01 -1.040 0.299
## time_value:EmergDec 1.900e-05 1.946e-05 0.976 0.329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01953 on 673 degrees of freedom
## Multiple R-squared: 0.09645, Adjusted R-squared: 0.09242
## F-statistic: 23.95 on 3 and 673 DF, p-value: 9.829e-15
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051649 -0.020051 0.000671 0.020410 0.060354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.195e+00 1.806e-01 -6.618 7.45e-11 ***
## time_value 6.952e-05 9.970e-06 6.974 7.40e-12 ***
## EmergDec -8.364e-01 4.909e-01 -1.704 0.0889 .
## time_value:EmergDec 4.303e-05 2.667e-05 1.614 0.1071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02638 on 673 degrees of freedom
## Multiple R-squared: 0.1798, Adjusted R-squared: 0.1761
## F-statistic: 49.18 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056863 -0.023122 0.002713 0.022197 0.058287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.306e+00 1.947e-01 -6.706 4.25e-11 ***
## time_value 7.571e-05 1.075e-05 7.044 4.62e-12 ***
## EmergDec -1.690e+00 5.292e-01 -3.193 0.00147 **
## time_value:EmergDec 8.925e-05 2.875e-05 3.104 0.00199 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02844 on 673 degrees of freedom
## Multiple R-squared: 0.1764, Adjusted R-squared: 0.1728
## F-statistic: 48.06 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050288 -0.018031 0.003059 0.019714 0.051967
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.807e-01 1.710e-01 -3.982 7.59e-05 ***
## time_value 4.110e-05 9.437e-06 4.355 1.54e-05 ***
## EmergDec -7.464e-01 4.647e-01 -1.606 0.109
## time_value:EmergDec 3.854e-05 2.524e-05 1.527 0.127
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02497 on 673 degrees of freedom
## Multiple R-squared: 0.1754, Adjusted R-squared: 0.1717
## F-statistic: 47.71 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053725 -0.015531 0.000963 0.021926 0.080801
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.820e-01 1.911e-01 -4.092 4.80e-05 ***
## time_value 4.683e-05 1.055e-05 4.439 1.06e-05 ***
## EmergDec -7.158e-02 5.024e-01 -0.142 0.887
## time_value:EmergDec 1.582e-06 2.730e-05 0.058 0.954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02754 on 673 degrees of freedom
## Multiple R-squared: 0.2, Adjusted R-squared: 0.1964
## F-statistic: 56.07 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.028441 -0.009280 0.001983 0.008393 0.029671
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.843e-01 8.295e-02 -5.839 8.16e-09 ***
## time_value 2.946e-05 4.579e-06 6.433 2.37e-10 ***
## EmergDec -5.228e-02 2.217e-01 -0.236 0.814
## time_value:EmergDec 2.047e-06 1.205e-05 0.170 0.865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01203 on 673 degrees of freedom
## Multiple R-squared: 0.096, Adjusted R-squared: 0.09197
## F-statistic: 23.82 on 3 and 673 DF, p-value: 1.159e-14
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.030037 -0.011140 0.003470 0.009298 0.035078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.842e-01 9.037e-02 -5.358 1.16e-07 ***
## time_value 2.925e-05 4.989e-06 5.864 7.10e-09 ***
## EmergDec -4.526e-01 2.436e-01 -1.858 0.0636 .
## time_value:EmergDec 2.367e-05 1.323e-05 1.789 0.0741 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01316 on 673 degrees of freedom
## Multiple R-squared: 0.1106, Adjusted R-squared: 0.1067
## F-statistic: 27.91 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043116 -0.013234 0.000096 0.016801 0.067217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.929e-01 1.516e-01 -5.231 2.25e-07 ***
## time_value 4.710e-05 8.368e-06 5.628 2.67e-08 ***
## EmergDec -1.566e-01 3.920e-01 -0.400 0.690
## time_value:EmergDec 6.768e-06 2.131e-05 0.318 0.751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02169 on 673 degrees of freedom
## Multiple R-squared: 0.1537, Adjusted R-squared: 0.1499
## F-statistic: 40.75 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04764 -0.01787 0.00142 0.01794 0.07458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.822e-01 1.593e-01 -5.537 4.41e-08 ***
## time_value 5.192e-05 8.795e-06 5.903 5.66e-09 ***
## EmergDec -5.092e-01 4.189e-01 -1.216 0.224
## time_value:EmergDec 2.586e-05 2.276e-05 1.136 0.256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02296 on 673 degrees of freedom
## Multiple R-squared: 0.1407, Adjusted R-squared: 0.1369
## F-statistic: 36.74 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044599 -0.018007 0.003906 0.016681 0.047811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.106e-01 1.472e-01 -6.187 1.06e-09 ***
## time_value 5.332e-05 8.123e-06 6.564 1.05e-10 ***
## EmergDec -9.792e-01 4.068e-01 -2.407 0.0163 *
## time_value:EmergDec 5.141e-05 2.210e-05 2.327 0.0203 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02164 on 673 degrees of freedom
## Multiple R-squared: 0.1504, Adjusted R-squared: 0.1466
## F-statistic: 39.71 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033651 -0.012569 0.002483 0.010863 0.044479
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.484e-01 1.125e-01 -3.096 0.002045 **
## time_value 2.201e-05 6.213e-06 3.542 0.000425 ***
## EmergDec 7.073e-02 2.935e-01 0.241 0.809616
## time_value:EmergDec -4.725e-06 1.595e-05 -0.296 0.767147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01616 on 673 degrees of freedom
## Multiple R-squared: 0.08468, Adjusted R-squared: 0.0806
## F-statistic: 20.75 on 3 and 673 DF, p-value: 7.195e-13
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046586 -0.015434 0.001733 0.016900 0.066284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.280e-01 1.526e-01 -6.081 2.00e-09 ***
## time_value 5.447e-05 8.425e-06 6.466 1.94e-10 ***
## EmergDec -1.178e-01 3.915e-01 -0.301 0.764
## time_value:EmergDec 4.532e-06 2.128e-05 0.213 0.831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02176 on 673 degrees of freedom
## Multiple R-squared: 0.1644, Adjusted R-squared: 0.1607
## F-statistic: 44.14 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042665 -0.014121 0.001247 0.016700 0.057840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.051e-01 1.463e-01 -5.503 5.32e-08 ***
## time_value 4.771e-05 8.077e-06 5.906 5.55e-09 ***
## EmergDec 2.136e-01 3.846e-01 0.555 0.579
## time_value:EmergDec -1.324e-05 2.090e-05 -0.633 0.527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02108 on 673 degrees of freedom
## Multiple R-squared: 0.1419, Adjusted R-squared: 0.1381
## F-statistic: 37.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038268 -0.018426 0.004922 0.014957 0.032305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.073e-01 1.272e-01 -4.773 2.23e-06 ***
## time_value 3.627e-05 7.023e-06 5.164 3.18e-07 ***
## EmergDec -9.046e-01 3.458e-01 -2.616 0.0091 **
## time_value:EmergDec 4.794e-05 1.879e-05 2.552 0.0109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01858 on 673 degrees of freedom
## Multiple R-squared: 0.1012, Adjusted R-squared: 0.09724
## F-statistic: 25.27 on 3 and 673 DF, p-value: 1.678e-15
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049642 -0.018993 0.001042 0.019406 0.059793
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.211e+00 1.617e-01 -7.488 2.20e-13 ***
## time_value 7.019e-05 8.926e-06 7.864 1.48e-14 ***
## EmergDec -1.159e+00 4.395e-01 -2.637 0.00856 **
## time_value:EmergDec 6.084e-05 2.388e-05 2.548 0.01105 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02362 on 673 degrees of freedom
## Multiple R-squared: 0.1715, Adjusted R-squared: 0.1678
## F-statistic: 46.43 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04347 -0.01906 0.00437 0.01691 0.04173
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.813e-01 1.477e-01 -5.291 1.65e-07 ***
## time_value 4.615e-05 8.151e-06 5.662 2.21e-08 ***
## EmergDec -9.189e-01 3.980e-01 -2.309 0.0213 *
## time_value:EmergDec 4.826e-05 2.162e-05 2.232 0.0259 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0215 on 673 degrees of freedom
## Multiple R-squared: 0.1382, Adjusted R-squared: 0.1343
## F-statistic: 35.96 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043244 -0.017279 0.002979 0.014712 0.042935
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.086e-01 1.446e-01 -6.285 5.90e-10 ***
## time_value 5.321e-05 7.981e-06 6.667 5.45e-11 ***
## EmergDec -8.336e-01 3.929e-01 -2.121 0.0342 *
## time_value:EmergDec 4.333e-05 2.135e-05 2.030 0.0428 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02112 on 673 degrees of freedom
## Multiple R-squared: 0.1891, Adjusted R-squared: 0.1855
## F-statistic: 52.32 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044813 -0.019163 0.005105 0.015792 0.051903
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.653e-01 1.472e-01 -3.162 0.001637 **
## time_value 2.890e-05 8.125e-06 3.557 0.000401 ***
## EmergDec -4.114e-01 3.775e-01 -1.090 0.276249
## time_value:EmergDec 2.121e-05 2.052e-05 1.034 0.301732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02099 on 673 degrees of freedom
## Multiple R-squared: 0.07866, Adjusted R-squared: 0.07455
## F-statistic: 19.15 on 3 and 673 DF, p-value: 6.3e-12
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045308 -0.015766 0.004225 0.016106 0.042491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.442e-01 1.456e-01 -5.111 4.19e-07 ***
## time_value 4.422e-05 8.039e-06 5.502 5.35e-08 ***
## EmergDec -2.862e-01 3.925e-01 -0.729 0.466
## time_value:EmergDec 1.377e-05 2.132e-05 0.646 0.519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0212 on 673 degrees of freedom
## Multiple R-squared: 0.1718, Adjusted R-squared: 0.1681
## F-statistic: 46.54 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041627 -0.013583 0.001789 0.013311 0.049354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.197e-01 1.285e-01 -7.158 2.15e-12 ***
## time_value 5.406e-05 7.092e-06 7.622 8.53e-14 ***
## EmergDec -2.043e-01 3.492e-01 -0.585 0.559
## time_value:EmergDec 9.454e-06 1.897e-05 0.498 0.618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01877 on 673 degrees of freedom
## Multiple R-squared: 0.1614, Adjusted R-squared: 0.1576
## F-statistic: 43.16 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037853 -0.013000 0.002802 0.012482 0.037309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.924e-01 1.265e-01 -3.101 0.002006 **
## time_value 2.455e-05 6.986e-06 3.514 0.000471 ***
## EmergDec 3.543e-02 3.093e-01 0.115 0.908836
## time_value:EmergDec -3.033e-06 1.682e-05 -0.180 0.856955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01767 on 673 degrees of freedom
## Multiple R-squared: 0.1178, Adjusted R-squared: 0.1139
## F-statistic: 29.95 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055791 -0.019139 0.002927 0.019673 0.055683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.381e+00 1.830e-01 -7.544 1.49e-13 ***
## time_value 7.990e-05 1.010e-05 7.909 1.07e-14 ***
## EmergDec -2.920e-02 4.933e-01 -0.059 0.953
## time_value:EmergDec -1.144e-06 2.680e-05 -0.043 0.966
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02664 on 673 degrees of freedom
## Multiple R-squared: 0.2208, Adjusted R-squared: 0.2174
## F-statistic: 63.58 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044567 -0.016782 0.002939 0.014983 0.042522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.634e-01 1.358e-01 -7.092 3.34e-12 ***
## time_value 5.627e-05 7.498e-06 7.506 1.94e-13 ***
## EmergDec -4.389e-01 3.787e-01 -1.159 0.247
## time_value:EmergDec 2.214e-05 2.057e-05 1.077 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02004 on 673 degrees of freedom
## Multiple R-squared: 0.1531, Adjusted R-squared: 0.1493
## F-statistic: 40.55 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037799 -0.014816 0.001688 0.013655 0.038296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.732e-01 1.121e-01 -5.113 4.14e-07 ***
## time_value 3.447e-05 6.189e-06 5.570 3.68e-08 ***
## EmergDec -1.034e+00 3.047e-01 -3.392 0.000735 ***
## time_value:EmergDec 5.508e-05 1.655e-05 3.327 0.000925 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01638 on 673 degrees of freedom
## Multiple R-squared: 0.1091, Adjusted R-squared: 0.1051
## F-statistic: 27.47 on 3 and 673 DF, p-value: < 2.2e-16School Closure
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*SchoolClose,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.025117 -0.008892 -0.000403 0.008317 0.047289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.931e-01 5.238e-02 -5.595 3.21e-08 ***
## time_value 1.888e-05 2.879e-06 6.557 1.09e-10 ***
## SchoolClose 8.031e-02 5.437e-01 0.148 0.883
## time_value:SchoolClose -4.788e-06 2.954e-05 -0.162 0.871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01317 on 673 degrees of freedom
## Multiple R-squared: 0.07224, Adjusted R-squared: 0.06811
## F-statistic: 17.47 on 3 and 673 DF, p-value: 6.242e-11
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042844 -0.021308 0.002003 0.018371 0.068852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.996e-02 9.608e-02 -0.936 0.349
## time_value 8.118e-06 5.276e-06 1.539 0.124
## SchoolClose -1.695e+00 1.590e+00 -1.066 0.287
## time_value:SchoolClose 9.098e-05 8.644e-05 1.052 0.293
##
## Residual standard error: 0.02528 on 673 degrees of freedom
## Multiple R-squared: 0.08155, Adjusted R-squared: 0.07745
## F-statistic: 19.92 on 3 and 673 DF, p-value: 2.228e-12
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045456 -0.019334 0.004116 0.015847 0.051221
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.124e-01 9.573e-02 -3.263 0.001157 **
## time_value 2.029e-05 5.266e-06 3.853 0.000128 ***
## SchoolClose -4.857e-01 7.126e-01 -0.682 0.495716
## time_value:SchoolClose 2.519e-05 3.870e-05 0.651 0.515427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02261 on 673 degrees of freedom
## Multiple R-squared: 0.113, Adjusted R-squared: 0.109
## F-statistic: 28.58 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.031563 -0.013238 0.001502 0.011487 0.048300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.517e-02 6.630e-02 -0.229 0.819
## time_value 3.565e-06 3.646e-06 0.978 0.329
## SchoolClose -7.634e-01 5.310e-01 -1.438 0.151
## time_value:SchoolClose 4.079e-05 2.884e-05 1.414 0.158
##
## Residual standard error: 0.01594 on 673 degrees of freedom
## Multiple R-squared: 0.08849, Adjusted R-squared: 0.08443
## F-statistic: 21.78 on 3 and 673 DF, p-value: 1.802e-13
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034792 -0.012221 0.003735 0.011346 0.028839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.287e-01 1.046e-01 -3.144 0.00174 **
## time_value 2.098e-05 5.772e-06 3.634 0.00030 ***
## SchoolClose -5.146e-01 2.842e-01 -1.811 0.07064 .
## time_value:SchoolClose 2.712e-05 1.544e-05 1.756 0.07948 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01527 on 673 degrees of freedom
## Multiple R-squared: 0.07866, Adjusted R-squared: 0.07456
## F-statistic: 19.15 on 3 and 673 DF, p-value: 6.289e-12
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037560 -0.016524 0.002225 0.013083 0.049252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.332e-02 7.766e-02 1.073 0.284
## time_value -1.476e-06 4.267e-06 -0.346 0.730
## SchoolClose -1.515e+00 9.643e-01 -1.571 0.117
## time_value:SchoolClose 8.149e-05 5.240e-05 1.555 0.120
##
## Residual standard error: 0.01982 on 673 degrees of freedom
## Multiple R-squared: 0.08569, Adjusted R-squared: 0.08162
## F-statistic: 21.03 on 3 and 673 DF, p-value: 4.981e-13
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04448 -0.02326 0.00045 0.01705 0.06199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.457e-01 1.033e-01 2.379 0.0177 *
## time_value -1.017e-05 5.676e-06 -1.791 0.0737 .
## SchoolClose -8.954e-01 1.084e+00 -0.826 0.4093
## time_value:SchoolClose 4.749e-05 5.893e-05 0.806 0.4206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02597 on 673 degrees of freedom
## Multiple R-squared: 0.1198, Adjusted R-squared: 0.1158
## F-statistic: 30.52 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035311 -0.007202 0.001442 0.010093 0.041163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.560e-01 1.125e-01 -6.720 3.86e-11 ***
## time_value 4.462e-05 6.209e-06 7.185 1.78e-12 ***
## SchoolClose 1.878e-02 3.136e-01 0.060 0.952
## time_value:SchoolClose -2.345e-06 1.703e-05 -0.138 0.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0166 on 673 degrees of freedom
## Multiple R-squared: 0.1381, Adjusted R-squared: 0.1343
## F-statistic: 35.96 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043534 -0.014009 0.003783 0.014864 0.041470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.889e-01 1.389e-01 -5.678 2.03e-08 ***
## time_value 4.667e-05 7.669e-06 6.086 1.95e-09 ***
## SchoolClose -1.236e-01 3.776e-01 -0.327 0.744
## time_value:SchoolClose 4.915e-06 2.051e-05 0.240 0.811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02029 on 673 degrees of freedom
## Multiple R-squared: 0.1857, Adjusted R-squared: 0.182
## F-statistic: 51.15 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034182 -0.017634 0.004396 0.012359 0.037956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.189e-01 7.430e-02 -1.600 0.1101
## time_value 9.250e-06 4.084e-06 2.265 0.0238 *
## SchoolClose -7.623e-01 7.318e-01 -1.042 0.2979
## time_value:SchoolClose 4.059e-05 3.976e-05 1.021 0.3077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01851 on 673 degrees of freedom
## Multiple R-squared: 0.08542, Adjusted R-squared: 0.08134
## F-statistic: 20.95 on 3 and 673 DF, p-value: 5.495e-13
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03610 -0.02429 0.00384 0.01544 0.04623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.591e-02 8.511e-02 -0.304 0.761
## time_value 4.233e-06 4.676e-06 0.905 0.366
## SchoolClose -1.049e+00 1.062e+00 -0.987 0.324
## time_value:SchoolClose 5.602e-05 5.774e-05 0.970 0.332
##
## Residual standard error: 0.02184 on 673 degrees of freedom
## Multiple R-squared: 0.08474, Adjusted R-squared: 0.08066
## F-statistic: 20.77 on 3 and 673 DF, p-value: 7.024e-13
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0113905 -0.0040139 0.0005136 0.0035754 0.0163294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.802e-01 2.379e-02 -7.573 1.21e-13 ***
## time_value 1.145e-05 1.310e-06 8.742 < 2e-16 ***
## SchoolClose -1.119e-01 1.324e-01 -0.845 0.398
## time_value:SchoolClose 5.919e-06 7.190e-06 0.823 0.411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005177 on 673 degrees of freedom
## Multiple R-squared: 0.1095, Adjusted R-squared: 0.1055
## F-statistic: 27.58 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061225 -0.024878 0.003637 0.024381 0.061308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.314e+00 2.027e-01 -6.485 1.72e-10 ***
## time_value 7.635e-05 1.119e-05 6.825 1.96e-11 ***
## SchoolClose -1.674e+00 5.649e-01 -2.963 0.00315 **
## time_value:SchoolClose 8.813e-05 3.069e-05 2.872 0.00420 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0299 on 673 degrees of freedom
## Multiple R-squared: 0.192, Adjusted R-squared: 0.1884
## F-statistic: 53.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034295 -0.016697 0.002978 0.012885 0.042115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.324e-02 6.997e-02 -1.047 0.2956
## time_value 6.920e-06 3.843e-06 1.800 0.0722 .
## SchoolClose -8.305e-01 1.019e+00 -0.815 0.4152
## time_value:SchoolClose 4.451e-05 5.538e-05 0.804 0.4219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01815 on 673 degrees of freedom
## Multiple R-squared: 0.04808, Adjusted R-squared: 0.04383
## F-statistic: 11.33 on 3 and 673 DF, p-value: 2.946e-07
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044303 -0.022061 -0.001736 0.014212 0.074542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.195e-01 1.032e-01 3.094 0.00206 **
## time_value -1.428e-05 5.670e-06 -2.518 0.01202 *
## SchoolClose -8.430e-01 1.683e+00 -0.501 0.61650
## time_value:SchoolClose 4.463e-05 9.150e-05 0.488 0.62587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02716 on 673 degrees of freedom
## Multiple R-squared: 0.1053, Adjusted R-squared: 0.1013
## F-statistic: 26.41 on 3 and 673 DF, p-value: 3.705e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034158 -0.021714 0.002862 0.014886 0.048719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.734e-02 8.079e-02 -0.338 0.735
## time_value 4.368e-06 4.437e-06 0.984 0.325
## SchoolClose -1.110e+00 1.254e+00 -0.885 0.377
## time_value:SchoolClose 5.936e-05 6.821e-05 0.870 0.384
##
## Residual standard error: 0.02114 on 673 degrees of freedom
## Multiple R-squared: 0.08196, Adjusted R-squared: 0.07787
## F-statistic: 20.03 on 3 and 673 DF, p-value: 1.919e-12
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042916 -0.023513 -0.002194 0.018450 0.066477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.281e-01 9.820e-02 1.304 0.193
## time_value -3.878e-06 5.390e-06 -0.720 0.472
## SchoolClose 5.113e-01 2.829e+00 0.181 0.857
## time_value:SchoolClose -2.901e-05 1.540e-04 -0.188 0.851
##
## Residual standard error: 0.02658 on 673 degrees of freedom
## Multiple R-squared: 0.06364, Adjusted R-squared: 0.05947
## F-statistic: 15.25 on 3 and 673 DF, p-value: 1.31e-09
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048063 -0.018784 0.003916 0.018701 0.045893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.321e-01 1.563e-01 -5.965 3.96e-09 ***
## time_value 5.462e-05 8.626e-06 6.333 4.41e-10 ***
## SchoolClose -5.102e-01 4.356e-01 -1.171 0.242
## time_value:SchoolClose 2.577e-05 2.366e-05 1.089 0.277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02306 on 673 degrees of freedom
## Multiple R-squared: 0.1599, Adjusted R-squared: 0.1561
## F-statistic: 42.68 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034816 -0.019483 0.000854 0.015404 0.053231
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.061e-01 8.067e-02 -1.316 0.1887
## time_value 8.900e-06 4.430e-06 2.009 0.0449 *
## SchoolClose -1.071e+00 1.258e+00 -0.851 0.3950
## time_value:SchoolClose 5.732e-05 6.844e-05 0.838 0.4026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02121 on 673 degrees of freedom
## Multiple R-squared: 0.07196, Adjusted R-squared: 0.06782
## F-statistic: 17.39 on 3 and 673 DF, p-value: 6.909e-11
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040437 -0.017954 -0.000471 0.015374 0.056786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.604e-01 9.721e-02 1.650 0.0995 .
## time_value -5.498e-06 5.346e-06 -1.028 0.3042
## SchoolClose -9.668e-01 7.866e-01 -1.229 0.2194
## time_value:SchoolClose 5.144e-05 4.272e-05 1.204 0.2290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02338 on 673 degrees of freedom
## Multiple R-squared: 0.1252, Adjusted R-squared: 0.1213
## F-statistic: 32.11 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041668 -0.019645 0.000774 0.015749 0.055280
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.793e-01 9.258e-02 3.017 0.00265 **
## time_value -1.227e-05 5.087e-06 -2.411 0.01617 *
## SchoolClose -8.268e-01 1.025e+00 -0.806 0.42035
## time_value:SchoolClose 4.395e-05 5.573e-05 0.789 0.43060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02347 on 673 degrees of freedom
## Multiple R-squared: 0.1137, Adjusted R-squared: 0.1098
## F-statistic: 28.79 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033823 -0.018097 0.000302 0.012867 0.051591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.569e-02 7.815e-02 0.841 0.401
## time_value -5.267e-07 4.294e-06 -0.123 0.902
## SchoolClose -7.497e-01 9.608e-01 -0.780 0.435
## time_value:SchoolClose 3.998e-05 5.218e-05 0.766 0.444
##
## Residual standard error: 0.01973 on 673 degrees of freedom
## Multiple R-squared: 0.06627, Adjusted R-squared: 0.06211
## F-statistic: 15.92 on 3 and 673 DF, p-value: 5.193e-10
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034263 -0.020186 0.000178 0.013701 0.050538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.642e-02 8.004e-02 0.705 0.481
## time_value -1.495e-07 4.396e-06 -0.034 0.973
## SchoolClose -7.999e-01 1.208e+00 -0.662 0.508
## time_value:SchoolClose 4.254e-05 6.571e-05 0.647 0.518
##
## Residual standard error: 0.02095 on 673 degrees of freedom
## Multiple R-squared: 0.0859, Adjusted R-squared: 0.08183
## F-statistic: 21.08 on 3 and 673 DF, p-value: 4.612e-13
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043036 -0.020898 -0.001645 0.014876 0.070027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.135e-01 1.097e-01 1.947 0.0519 .
## time_value -8.351e-06 6.027e-06 -1.386 0.1663
## SchoolClose -4.846e-01 1.121e+00 -0.432 0.6656
## time_value:SchoolClose 2.504e-05 6.089e-05 0.411 0.6811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02744 on 673 degrees of freedom
## Multiple R-squared: 0.1247, Adjusted R-squared: 0.1208
## F-statistic: 31.95 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043329 -0.023873 0.002436 0.016271 0.066971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.206e-03 1.051e-01 -0.069 0.945
## time_value 3.780e-06 5.774e-06 0.655 0.513
## SchoolClose -1.116e+00 1.422e+00 -0.785 0.433
## time_value:SchoolClose 5.935e-05 7.726e-05 0.768 0.443
##
## Residual standard error: 0.02707 on 673 degrees of freedom
## Multiple R-squared: 0.09811, Adjusted R-squared: 0.09409
## F-statistic: 24.4 on 3 and 673 DF, p-value: 5.339e-15
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03692 -0.02305 0.00120 0.01783 0.05186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.481e-02 8.486e-02 -0.646 0.519
## time_value 6.006e-06 4.658e-06 1.290 0.198
## SchoolClose 2.595e-01 2.244e+00 0.116 0.908
## time_value:SchoolClose -1.513e-05 1.221e-04 -0.124 0.901
##
## Residual standard error: 0.02288 on 673 degrees of freedom
## Multiple R-squared: 0.05823, Adjusted R-squared: 0.05403
## F-statistic: 13.87 on 3 and 673 DF, p-value: 8.741e-09
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.032636 -0.016939 0.000639 0.012870 0.042601
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.509e-03 6.464e-02 0.054 0.957
## time_value 2.918e-06 3.547e-06 0.823 0.411
## SchoolClose 7.281e-01 2.838e+00 0.257 0.798
## time_value:SchoolClose -4.013e-05 1.545e-04 -0.260 0.795
##
## Residual standard error: 0.01769 on 673 degrees of freedom
## Multiple R-squared: 0.017, Adjusted R-squared: 0.01262
## F-statistic: 3.88 on 3 and 673 DF, p-value: 0.009103
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.039627 -0.017474 0.004601 0.015004 0.039468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.609e-01 1.318e-01 -4.256 2.37e-05 ***
## time_value 3.375e-05 7.274e-06 4.640 4.20e-06 ***
## SchoolClose -6.259e-01 3.674e-01 -1.704 0.0889 .
## time_value:SchoolClose 3.273e-05 1.995e-05 1.640 0.1014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01945 on 673 degrees of freedom
## Multiple R-squared: 0.1045, Adjusted R-squared: 0.1005
## F-statistic: 26.17 on 3 and 673 DF, p-value: 5.088e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041606 -0.023394 0.000055 0.018009 0.073114
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.726e-02 1.082e-01 -0.160 0.873
## time_value 4.341e-06 5.943e-06 0.730 0.465
## SchoolClose 7.478e-01 1.316e+00 0.568 0.570
## time_value:SchoolClose -4.194e-05 7.154e-05 -0.586 0.558
##
## Residual standard error: 0.02773 on 673 degrees of freedom
## Multiple R-squared: 0.09371, Adjusted R-squared: 0.08967
## F-statistic: 23.19 on 3 and 673 DF, p-value: 2.688e-14
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048286 -0.027128 0.002659 0.020617 0.070552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.057e-01 1.156e-01 -1.779 0.0757 .
## time_value 1.483e-05 6.353e-06 2.334 0.0199 *
## SchoolClose -4.115e-01 1.620e+00 -0.254 0.7996
## time_value:SchoolClose 2.075e-05 8.804e-05 0.236 0.8137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02964 on 673 degrees of freedom
## Multiple R-squared: 0.1054, Adjusted R-squared: 0.1014
## F-statistic: 26.43 on 3 and 673 DF, p-value: 3.588e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04499 -0.02511 0.00061 0.01657 0.06291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.234e-01 1.008e-01 3.209 0.0014 **
## time_value -1.446e-05 5.537e-06 -2.612 0.0092 **
## SchoolClose -3.933e-01 1.315e+00 -0.299 0.7649
## time_value:SchoolClose 2.032e-05 7.147e-05 0.284 0.7763
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02603 on 673 degrees of freedom
## Multiple R-squared: 0.1039, Adjusted R-squared: 0.09995
## F-statistic: 26.02 on 3 and 673 DF, p-value: 6.173e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049624 -0.021335 0.000465 0.019175 0.066406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.377e-01 1.166e-01 2.896 0.0039 **
## time_value -1.511e-05 6.413e-06 -2.356 0.0187 *
## SchoolClose -1.470e+00 1.001e+00 -1.469 0.1423
## time_value:SchoolClose 7.849e-05 5.436e-05 1.444 0.1492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02834 on 673 degrees of freedom
## Multiple R-squared: 0.1526, Adjusted R-squared: 0.1489
## F-statistic: 40.41 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.028203 -0.009554 0.002087 0.008331 0.029876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.693e-01 8.141e-02 -5.765 1.25e-08 ***
## time_value 2.863e-05 4.494e-06 6.370 3.50e-10 ***
## SchoolClose -1.629e-01 2.269e-01 -0.718 0.473
## time_value:SchoolClose 8.047e-06 1.233e-05 0.653 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01201 on 673 degrees of freedom
## Multiple R-squared: 0.09909, Adjusted R-squared: 0.09508
## F-statistic: 24.68 on 3 and 673 DF, p-value: 3.714e-15
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.027625 -0.012250 0.001805 0.010129 0.034902
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.525e-02 5.072e-02 -0.301 0.764
## time_value 3.300e-06 2.785e-06 1.185 0.236
## SchoolClose -6.560e-02 1.065e+00 -0.062 0.951
## time_value:SchoolClose 3.016e-06 5.795e-05 0.052 0.959
##
## Residual standard error: 0.01356 on 673 degrees of freedom
## Multiple R-squared: 0.05553, Adjusted R-squared: 0.05132
## F-statistic: 13.19 on 3 and 673 DF, p-value: 2.239e-08
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037158 -0.016367 0.000829 0.014118 0.059762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.048e-02 9.002e-02 0.450 0.653
## time_value 9.949e-07 4.949e-06 0.201 0.841
## SchoolClose -9.834e-01 8.316e-01 -1.182 0.237
## time_value:SchoolClose 5.235e-05 4.518e-05 1.159 0.247
##
## Residual standard error: 0.02217 on 673 degrees of freedom
## Multiple R-squared: 0.116, Adjusted R-squared: 0.1121
## F-statistic: 29.45 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038255 -0.024981 0.001425 0.015901 0.055934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.831e-02 9.078e-02 0.532 0.595
## time_value 4.324e-07 4.986e-06 0.087 0.931
## SchoolClose -7.495e-01 1.349e+00 -0.556 0.579
## time_value:SchoolClose 3.969e-05 7.334e-05 0.541 0.589
##
## Residual standard error: 0.02371 on 673 degrees of freedom
## Multiple R-squared: 0.08361, Adjusted R-squared: 0.07953
## F-statistic: 20.47 on 3 and 673 DF, p-value: 1.058e-12
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038585 -0.018001 0.002417 0.013497 0.056696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.592e-01 8.876e-02 -1.793 0.0734 .
## time_value 1.175e-05 4.879e-06 2.408 0.0163 *
## SchoolClose -5.922e-01 9.201e-01 -0.644 0.5200
## time_value:SchoolClose 3.108e-05 5.000e-05 0.622 0.5344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02228 on 673 degrees of freedom
## Multiple R-squared: 0.09965, Adjusted R-squared: 0.09564
## F-statistic: 24.83 on 3 and 673 DF, p-value: 3.024e-15
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03261 -0.01404 0.00160 0.01092 0.04154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.568e-01 6.287e-02 2.495 0.0128 *
## time_value -5.945e-06 3.453e-06 -1.722 0.0856 .
## SchoolClose -3.376e-01 9.348e-01 -0.361 0.7181
## time_value:SchoolClose 1.786e-05 5.083e-05 0.351 0.7254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01643 on 673 degrees of freedom
## Multiple R-squared: 0.05377, Adjusted R-squared: 0.04956
## F-statistic: 12.75 on 3 and 673 DF, p-value: 4.119e-08
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03754 -0.02236 0.00126 0.01411 0.05219
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.188e-01 8.696e-02 1.366 0.173
## time_value -3.447e-06 4.776e-06 -0.722 0.471
## SchoolClose -8.146e-01 1.293e+00 -0.630 0.529
## time_value:SchoolClose 4.329e-05 7.032e-05 0.616 0.538
##
## Residual standard error: 0.02273 on 673 degrees of freedom
## Multiple R-squared: 0.0885, Adjusted R-squared: 0.08444
## F-statistic: 21.78 on 3 and 673 DF, p-value: 1.798e-13
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036380 -0.013715 0.001115 0.014429 0.050941
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.318e-01 9.741e-02 -1.353 0.1766
## time_value 1.047e-05 5.362e-06 1.952 0.0514 .
## SchoolClose -5.204e-01 5.617e-01 -0.926 0.3546
## time_value:SchoolClose 2.719e-05 3.049e-05 0.892 0.3729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02145 on 673 degrees of freedom
## Multiple R-squared: 0.1114, Adjusted R-squared: 0.1074
## F-statistic: 28.11 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03142 -0.02089 0.00342 0.01351 0.03789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.916e-02 7.264e-02 -0.814 0.416
## time_value 5.934e-06 3.989e-06 1.488 0.137
## SchoolClose -4.859e-01 1.166e+00 -0.417 0.677
## time_value:SchoolClose 2.572e-05 6.342e-05 0.406 0.685
##
## Residual standard error: 0.0191 on 673 degrees of freedom
## Multiple R-squared: 0.05071, Adjusted R-squared: 0.04648
## F-statistic: 11.98 on 3 and 673 DF, p-value: 1.189e-07
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036370 -0.023816 -0.004372 0.019169 0.069533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.101e-03 9.316e-02 -0.023 0.982
## time_value 3.267e-06 5.111e-06 0.639 0.523
## SchoolClose 4.345e+00 5.585e+00 0.778 0.437
## time_value:SchoolClose -2.376e-04 3.042e-04 -0.781 0.435
##
## Residual standard error: 0.02562 on 673 degrees of freedom
## Multiple R-squared: 0.02498, Adjusted R-squared: 0.02063
## F-statistic: 5.747 on 3 and 673 DF, p-value: 0.0006948
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037606 -0.018078 0.003831 0.014805 0.048375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.958e-02 8.789e-02 -0.905 0.366
## time_value 7.326e-06 4.831e-06 1.517 0.130
## SchoolClose -5.781e-01 9.006e-01 -0.642 0.521
## time_value:SchoolClose 3.039e-05 4.894e-05 0.621 0.535
##
## Residual standard error: 0.02205 on 673 degrees of freedom
## Multiple R-squared: 0.09311, Adjusted R-squared: 0.08907
## F-statistic: 23.03 on 3 and 673 DF, p-value: 3.339e-14
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038054 -0.017100 0.002101 0.012625 0.054744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.366e-02 8.707e-02 0.157 0.875
## time_value 2.171e-06 4.784e-06 0.454 0.650
## SchoolClose -7.301e-01 1.039e+00 -0.702 0.483
## time_value:SchoolClose 3.858e-05 5.648e-05 0.683 0.495
##
## Residual standard error: 0.02216 on 673 degrees of freedom
## Multiple R-squared: 0.1069, Adjusted R-squared: 0.103
## F-statistic: 26.86 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044371 -0.018913 0.005882 0.015558 0.041097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.337e-01 1.415e-01 -3.065 0.002262 **
## time_value 2.715e-05 7.810e-06 3.476 0.000541 ***
## SchoolClose -7.910e-01 3.945e-01 -2.005 0.045328 *
## time_value:SchoolClose 4.178e-05 2.142e-05 1.950 0.051590 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02088 on 673 degrees of freedom
## Multiple R-squared: 0.08801, Adjusted R-squared: 0.08395
## F-statistic: 21.65 on 3 and 673 DF, p-value: 2.146e-13
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040307 -0.022240 0.002455 0.013502 0.051315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.152e-01 8.559e-02 2.515 0.0121 *
## time_value -8.864e-06 4.701e-06 -1.885 0.0598 .
## SchoolClose -1.301e-01 1.182e+00 -0.110 0.9124
## time_value:SchoolClose 6.177e-06 6.427e-05 0.096 0.9235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02223 on 673 degrees of freedom
## Multiple R-squared: 0.08951, Adjusted R-squared: 0.08545
## F-statistic: 22.05 on 3 and 673 DF, p-value: 1.244e-13
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035144 -0.012312 0.000354 0.011654 0.054604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.598e-01 8.570e-02 -3.031 0.002529 **
## time_value 1.755e-05 4.717e-06 3.721 0.000215 ***
## SchoolClose -4.408e-01 5.399e-01 -0.816 0.414555
## time_value:SchoolClose 2.292e-05 2.931e-05 0.782 0.434515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01933 on 673 degrees of freedom
## Multiple R-squared: 0.11, Adjusted R-squared: 0.106
## F-statistic: 27.73 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034026 -0.013816 0.001899 0.012363 0.041098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.621e-01 7.204e-02 2.250 0.0248 *
## time_value -6.125e-06 3.960e-06 -1.547 0.1224
## SchoolClose -6.595e-01 6.833e-01 -0.965 0.3348
## time_value:SchoolClose 3.512e-05 3.712e-05 0.946 0.3445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01784 on 673 degrees of freedom
## Multiple R-squared: 0.1008, Adjusted R-squared: 0.09682
## F-statistic: 25.15 on 3 and 673 DF, p-value: 1.961e-15
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045192 -0.024011 -0.000082 0.016040 0.070910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.265e-01 1.092e-01 2.074 0.0384 *
## time_value -9.027e-06 5.995e-06 -1.506 0.1326
## SchoolClose -2.170e-01 1.927e+00 -0.113 0.9104
## time_value:SchoolClose 1.054e-05 1.048e-04 0.101 0.9199
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02886 on 673 degrees of freedom
## Multiple R-squared: 0.08596, Adjusted R-squared: 0.08188
## F-statistic: 21.1 on 3 and 673 DF, p-value: 4.524e-13
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045018 -0.016787 0.002805 0.015025 0.042212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.949e-01 1.364e-01 -7.296 8.38e-13 ***
## time_value 5.802e-05 7.527e-06 7.708 4.60e-14 ***
## SchoolClose -3.882e-01 3.738e-01 -1.039 0.299
## time_value:SchoolClose 1.936e-05 2.030e-05 0.953 0.341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01999 on 673 degrees of freedom
## Multiple R-squared: 0.1579, Adjusted R-squared: 0.1542
## F-statistic: 42.07 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.031832 -0.014904 0.000532 0.012412 0.041768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.037e-01 6.338e-02 -1.637 0.102
## time_value 8.483e-06 3.480e-06 2.438 0.015 *
## SchoolClose -1.322e-01 1.282e+00 -0.103 0.918
## time_value:SchoolClose 6.521e-06 6.973e-05 0.094 0.926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01688 on 673 degrees of freedom
## Multiple R-squared: 0.05369, Adjusted R-squared: 0.04948
## F-statistic: 12.73 on 3 and 673 DF, p-value: 4.237e-08Bar Restriction
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*BarRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.024157 -0.009866 -0.000590 0.008085 0.049688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.094e-01 4.912e-02 -4.263 2.30e-05 ***
## time_value 1.424e-05 2.696e-06 5.283 1.72e-07 ***
## BarRestrict -4.531e-01 1.584e+00 -0.286 0.775
## time_value:BarRestrict 2.431e-05 8.620e-05 0.282 0.778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01333 on 673 degrees of freedom
## Multiple R-squared: 0.04844, Adjusted R-squared: 0.04419
## F-statistic: 11.42 on 3 and 673 DF, p-value: 2.603e-07
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054702 -0.020943 0.003719 0.018625 0.059624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.120e-01 1.636e-01 -5.575 3.59e-08 ***
## time_value 5.361e-05 9.029e-06 5.937 4.65e-09 ***
## BarRestrict -1.701e+00 4.679e-01 -3.636 0.000299 ***
## time_value:BarRestrict 9.048e-05 2.541e-05 3.561 0.000395 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02438 on 673 degrees of freedom
## Multiple R-squared: 0.1455, Adjusted R-squared: 0.1417
## F-statistic: 38.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044473 -0.020696 0.004237 0.017885 0.046803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.696e-01 1.504e-01 -5.117 4.06e-07 ***
## time_value 4.558e-05 8.301e-06 5.490 5.69e-08 ***
## BarRestrict -1.442e+00 4.302e-01 -3.352 0.000848 ***
## time_value:BarRestrict 7.668e-05 2.336e-05 3.282 0.001082 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02242 on 673 degrees of freedom
## Multiple R-squared: 0.128, Adjusted R-squared: 0.1241
## F-statistic: 32.92 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033596 -0.013210 0.002789 0.011723 0.044827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.358e-01 1.019e-01 -3.295 0.001036 **
## time_value 2.129e-05 5.623e-06 3.786 0.000167 ***
## BarRestrict -7.585e-01 3.242e-01 -2.340 0.019580 *
## time_value:BarRestrict 4.020e-05 1.759e-05 2.285 0.022607 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01581 on 673 degrees of freedom
## Multiple R-squared: 0.1034, Adjusted R-squared: 0.0994
## F-statistic: 25.87 on 3 and 673 DF, p-value: 7.575e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034534 -0.012354 0.003733 0.011387 0.028814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.068e-01 1.037e-01 -2.960 0.003187 **
## time_value 1.976e-05 5.722e-06 3.454 0.000587 ***
## BarRestrict -5.610e-01 2.890e-01 -1.941 0.052654 .
## time_value:BarRestrict 2.965e-05 1.570e-05 1.889 0.059277 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0153 on 673 degrees of freedom
## Multiple R-squared: 0.07596, Adjusted R-squared: 0.07184
## F-statistic: 18.44 on 3 and 673 DF, p-value: 1.657e-11
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041660 -0.014137 0.003602 0.014433 0.041623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.753e-01 1.298e-01 -4.433 1.09e-05 ***
## time_value 3.497e-05 7.163e-06 4.881 1.32e-06 ***
## BarRestrict -7.953e-01 3.649e-01 -2.180 0.0296 *
## time_value:BarRestrict 4.175e-05 1.982e-05 2.107 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01922 on 673 degrees of freedom
## Multiple R-squared: 0.1405, Adjusted R-squared: 0.1366
## F-statistic: 36.66 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050213 -0.017160 0.002498 0.019499 0.050938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.618e-01 1.689e-01 -4.509 7.67e-06 ***
## time_value 4.557e-05 9.325e-06 4.887 1.28e-06 ***
## BarRestrict -7.121e-01 4.710e-01 -1.512 0.131
## time_value:BarRestrict 3.655e-05 2.558e-05 1.429 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02493 on 673 degrees of freedom
## Multiple R-squared: 0.1889, Adjusted R-squared: 0.1853
## F-statistic: 52.24 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035311 -0.007202 0.001442 0.010093 0.041163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.560e-01 1.125e-01 -6.720 3.86e-11 ***
## time_value 4.462e-05 6.209e-06 7.185 1.78e-12 ***
## BarRestrict 1.878e-02 3.136e-01 0.060 0.952
## time_value:BarRestrict -2.345e-06 1.703e-05 -0.138 0.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0166 on 673 degrees of freedom
## Multiple R-squared: 0.1381, Adjusted R-squared: 0.1343
## F-statistic: 35.96 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042956 -0.014172 0.003767 0.014745 0.042128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.406e-01 1.381e-01 -5.362 1.13e-07 ***
## time_value 4.400e-05 7.624e-06 5.771 1.20e-08 ***
## BarRestrict -1.975e-01 3.851e-01 -0.513 0.608
## time_value:BarRestrict 8.980e-06 2.091e-05 0.429 0.668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02038 on 673 degrees of freedom
## Multiple R-squared: 0.1786, Adjusted R-squared: 0.1749
## F-statistic: 48.77 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037218 -0.017960 0.005048 0.012582 0.035991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.844e-01 8.722e-02 -3.260 0.001168 **
## time_value 1.841e-05 4.804e-06 3.832 0.000139 ***
## BarRestrict -1.140e+00 4.453e-01 -2.561 0.010650 *
## time_value:BarRestrict 6.099e-05 2.417e-05 2.523 0.011861 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01834 on 673 degrees of freedom
## Multiple R-squared: 0.1025, Adjusted R-squared: 0.09853
## F-statistic: 25.63 on 3 and 673 DF, p-value: 1.044e-15
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040910 -0.020509 0.004988 0.016781 0.040865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.052e-01 1.421e-01 -3.556 0.000402 ***
## time_value 3.074e-05 7.839e-06 3.922 9.69e-05 ***
## BarRestrict -1.525e+00 4.244e-01 -3.593 0.000351 ***
## time_value:BarRestrict 8.143e-05 2.304e-05 3.534 0.000437 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02153 on 673 degrees of freedom
## Multiple R-squared: 0.1105, Adjusted R-squared: 0.1065
## F-statistic: 27.87 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0122371 -0.0037473 0.0004605 0.0036079 0.0158151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.548e-01 3.472e-02 -7.338 6.28e-13 ***
## time_value 1.558e-05 1.917e-06 8.129 2.09e-15 ***
## BarRestrict -1.156e-01 9.762e-02 -1.184 0.237
## time_value:BarRestrict 6.040e-06 5.302e-06 1.139 0.255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005141 on 673 degrees of freedom
## Multiple R-squared: 0.1216, Adjusted R-squared: 0.1177
## F-statistic: 31.07 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060955 -0.025076 0.003689 0.024380 0.061472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.298e+00 2.020e-01 -6.424 2.52e-10 ***
## time_value 7.542e-05 1.115e-05 6.765 2.91e-11 ***
## BarRestrict -1.746e+00 5.679e-01 -3.075 0.00219 **
## time_value:BarRestrict 9.207e-05 3.085e-05 2.985 0.00294 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02991 on 673 degrees of freedom
## Multiple R-squared: 0.1918, Adjusted R-squared: 0.1882
## F-statistic: 53.24 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.039247 -0.016400 0.003993 0.013700 0.039325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.805e-01 1.170e-01 -4.106 4.52e-05 ***
## time_value 2.945e-05 6.457e-06 4.561 6.04e-06 ***
## BarRestrict -1.025e+00 3.527e-01 -2.905 0.00379 **
## time_value:BarRestrict 5.462e-05 1.914e-05 2.853 0.00446 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01779 on 673 degrees of freedom
## Multiple R-squared: 0.08493, Adjusted R-squared: 0.08085
## F-statistic: 20.82 on 3 and 673 DF, p-value: 6.555e-13
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053548 -0.016150 0.002711 0.016111 0.057910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.152e+00 1.699e-01 -6.780 2.64e-11 ***
## time_value 6.711e-05 9.376e-06 7.158 2.15e-12 ***
## BarRestrict -2.565e-01 4.735e-01 -0.542 0.588
## time_value:BarRestrict 1.133e-05 2.572e-05 0.441 0.660
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02506 on 673 degrees of freedom
## Multiple R-squared: 0.2379, Adjusted R-squared: 0.2345
## F-statistic: 70.02 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042840 -0.018423 0.004236 0.016388 0.039710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.752e-01 1.382e-01 -5.608 3.0e-08 ***
## time_value 4.575e-05 7.631e-06 5.996 3.3e-09 ***
## BarRestrict -1.043e+00 3.854e-01 -2.707 0.00696 **
## time_value:BarRestrict 5.505e-05 2.093e-05 2.630 0.00874 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0204 on 673 degrees of freedom
## Multiple R-squared: 0.1451, Adjusted R-squared: 0.1413
## F-statistic: 38.07 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042767 -0.024601 -0.003248 0.018805 0.066957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.501e-01 9.826e-02 1.528 0.127
## time_value -5.110e-06 5.392e-06 -0.948 0.344
## BarRestrict -8.969e-01 3.850e+00 -0.233 0.816
## time_value:BarRestrict 4.761e-05 2.095e-04 0.227 0.820
##
## Residual standard error: 0.0267 on 673 degrees of freedom
## Multiple R-squared: 0.05514, Adjusted R-squared: 0.05093
## F-statistic: 13.09 on 3 and 673 DF, p-value: 2.56e-08
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048063 -0.018784 0.003916 0.018701 0.045893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.321e-01 1.563e-01 -5.965 3.96e-09 ***
## time_value 5.462e-05 8.626e-06 6.333 4.41e-10 ***
## BarRestrict -5.102e-01 4.356e-01 -1.171 0.242
## time_value:BarRestrict 2.577e-05 2.366e-05 1.089 0.277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02306 on 673 degrees of freedom
## Multiple R-squared: 0.1599, Adjusted R-squared: 0.1561
## F-statistic: 42.68 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040005 -0.017859 0.003537 0.015898 0.043926
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.795e-01 1.372e-01 -6.409 2.76e-10 ***
## time_value 5.169e-05 7.575e-06 6.824 1.97e-11 ***
## BarRestrict -1.129e+00 3.858e-01 -2.926 0.00354 **
## time_value:BarRestrict 5.968e-05 2.096e-05 2.848 0.00453 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02032 on 673 degrees of freedom
## Multiple R-squared: 0.1485, Adjusted R-squared: 0.1447
## F-statistic: 39.13 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045112 -0.014374 0.001635 0.017129 0.046951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.756e-01 1.525e-01 -4.429 1.10e-05 ***
## time_value 4.074e-05 8.418e-06 4.840 1.61e-06 ***
## BarRestrict -5.262e-01 4.288e-01 -1.227 0.220
## time_value:BarRestrict 2.669e-05 2.329e-05 1.146 0.252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02258 on 673 degrees of freedom
## Multiple R-squared: 0.1841, Adjusted R-squared: 0.1805
## F-statistic: 50.63 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046350 -0.013070 0.003793 0.015348 0.042190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.867e-01 1.505e-01 -5.227 2.30e-07 ***
## time_value 4.670e-05 8.307e-06 5.622 2.77e-08 ***
## BarRestrict -1.949e-01 4.196e-01 -0.464 0.642
## time_value:BarRestrict 8.567e-06 2.279e-05 0.376 0.707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02221 on 673 degrees of freedom
## Multiple R-squared: 0.2066, Adjusted R-squared: 0.2031
## F-statistic: 58.41 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038021 -0.016073 0.001985 0.014755 0.047114
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.187e-01 1.282e-01 -4.827 1.71e-06 ***
## time_value 3.734e-05 7.074e-06 5.279 1.75e-07 ***
## BarRestrict -4.529e-01 3.634e-01 -1.246 0.213
## time_value:BarRestrict 2.320e-05 1.974e-05 1.175 0.240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01904 on 673 degrees of freedom
## Multiple R-squared: 0.1308, Adjusted R-squared: 0.127
## F-statistic: 33.77 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038587 -0.014453 0.001388 0.014769 0.045932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.134e-01 1.006e-01 -3.116 0.001912 **
## time_value 2.032e-05 5.542e-06 3.668 0.000264 ***
## BarRestrict -1.228e+00 4.676e-01 -2.627 0.008807 **
## time_value:BarRestrict 6.548e-05 2.538e-05 2.580 0.010091 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02037 on 673 degrees of freedom
## Multiple R-squared: 0.1354, Adjusted R-squared: 0.1315
## F-statistic: 35.12 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05225 -0.01708 0.00211 0.01805 0.05508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.140e+00 1.732e-01 -6.583 9.30e-11 ***
## time_value 6.652e-05 9.557e-06 6.960 8.09e-12 ***
## BarRestrict -1.723e-01 4.868e-01 -0.354 0.723
## time_value:BarRestrict 6.741e-06 2.644e-05 0.255 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02564 on 673 degrees of freedom
## Multiple R-squared: 0.2357, Adjusted R-squared: 0.2323
## F-statistic: 69.18 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05447 -0.02198 0.00383 0.01987 0.05586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.471e-01 1.723e-01 -5.495 5.54e-08 ***
## time_value 5.578e-05 9.511e-06 5.865 7.05e-09 ***
## BarRestrict -1.580e+00 5.104e-01 -3.095 0.00205 **
## time_value:BarRestrict 8.359e-05 2.771e-05 3.017 0.00265 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02603 on 673 degrees of freedom
## Multiple R-squared: 0.1658, Adjusted R-squared: 0.1621
## F-statistic: 44.58 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043422 -0.021264 0.004372 0.017756 0.045205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.196e-01 1.470e-01 -4.216 2.82e-05 ***
## time_value 3.727e-05 8.110e-06 4.595 5.17e-06 ***
## BarRestrict -1.793e+00 4.391e-01 -4.084 4.97e-05 ***
## time_value:BarRestrict 9.596e-05 2.384e-05 4.026 6.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02227 on 673 degrees of freedom
## Multiple R-squared: 0.1071, Adjusted R-squared: 0.1031
## F-statistic: 26.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036836 -0.015069 0.002754 0.013072 0.038411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.689e-01 1.138e-01 -5.001 7.29e-07 ***
## time_value 3.461e-05 6.279e-06 5.512 5.07e-08 ***
## BarRestrict -6.767e-01 3.282e-01 -2.062 0.0396 *
## time_value:BarRestrict 3.570e-05 1.782e-05 2.003 0.0456 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01701 on 673 degrees of freedom
## Multiple R-squared: 0.09105, Adjusted R-squared: 0.087
## F-statistic: 22.47 on 3 and 673 DF, p-value: 7.102e-14
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.039575 -0.017283 0.004611 0.014811 0.039518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.573e-01 1.313e-01 -4.246 2.49e-05 ***
## time_value 3.354e-05 7.244e-06 4.630 4.38e-06 ***
## BarRestrict -6.680e-01 3.690e-01 -1.810 0.0707 .
## time_value:BarRestrict 3.501e-05 2.004e-05 1.747 0.0811 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01943 on 673 degrees of freedom
## Multiple R-squared: 0.1056, Adjusted R-squared: 0.1016
## F-statistic: 26.49 on 3 and 673 DF, p-value: 3.319e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040950 -0.025181 -0.005945 0.019199 0.076035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.955e-01 1.047e-01 1.867 0.0624 .
## time_value -7.459e-06 5.747e-06 -1.298 0.1947
## BarRestrict 7.618e-01 3.538e+00 0.215 0.8296
## time_value:BarRestrict -4.254e-05 1.926e-04 -0.221 0.8252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02845 on 673 degrees of freedom
## Multiple R-squared: 0.04647, Adjusted R-squared: 0.04222
## F-statistic: 10.93 on 3 and 673 DF, p-value: 5.116e-07
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055302 -0.025777 0.001748 0.021831 0.067621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.638e-01 1.306e-01 -3.550 0.000412 ***
## time_value 2.911e-05 7.190e-06 4.049 5.75e-05 ***
## BarRestrict -2.625e+00 8.918e-01 -2.943 0.003363 **
## time_value:BarRestrict 1.408e-04 4.839e-05 2.909 0.003747 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02928 on 673 degrees of freedom
## Multiple R-squared: 0.1273, Adjusted R-squared: 0.1234
## F-statistic: 32.73 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043611 -0.016199 0.001539 0.017494 0.060825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.458e-01 1.080e-01 1.351 0.177
## time_value -4.627e-06 5.939e-06 -0.779 0.436
## BarRestrict -6.511e-01 8.246e-01 -0.790 0.430
## time_value:BarRestrict 3.417e-05 4.479e-05 0.763 0.446
##
## Residual standard error: 0.02569 on 673 degrees of freedom
## Multiple R-squared: 0.1272, Adjusted R-squared: 0.1233
## F-statistic: 32.69 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053596 -0.015873 0.002651 0.021972 0.054609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.736e-01 1.842e-01 -4.199 3.04e-05 ***
## time_value 4.637e-05 1.017e-05 4.560 6.08e-06 ***
## BarRestrict -6.711e-01 5.135e-01 -1.307 0.192
## time_value:BarRestrict 3.401e-05 2.789e-05 1.219 0.223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02718 on 673 degrees of freedom
## Multiple R-squared: 0.2204, Adjusted R-squared: 0.2169
## F-statistic: 63.41 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.028080 -0.009430 0.002061 0.008308 0.029982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.615e-01 8.048e-02 -5.735 1.48e-08 ***
## time_value 2.819e-05 4.442e-06 6.347 4.03e-10 ***
## BarRestrict -2.362e-01 2.302e-01 -1.026 0.305
## time_value:BarRestrict 1.202e-05 1.250e-05 0.962 0.336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.012 on 673 degrees of freedom
## Multiple R-squared: 0.1017, Adjusted R-squared: 0.09773
## F-statistic: 25.41 on 3 and 673 DF, p-value: 1.402e-15
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.029430 -0.011550 0.003415 0.009343 0.034449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.340e-01 8.775e-02 -4.946 9.59e-07 ***
## time_value 2.647e-05 4.843e-06 5.466 6.50e-08 ***
## BarRestrict -6.631e-01 2.553e-01 -2.597 0.00962 **
## time_value:BarRestrict 3.512e-05 1.387e-05 2.533 0.01153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01317 on 673 degrees of freedom
## Multiple R-squared: 0.1091, Adjusted R-squared: 0.1052
## F-statistic: 27.49 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042965 -0.013417 0.001414 0.016665 0.050083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.822e-01 1.447e-01 -5.405 9.01e-08 ***
## time_value 4.651e-05 7.988e-06 5.822 8.99e-09 ***
## BarRestrict -7.390e-01 4.034e-01 -1.832 0.0674 .
## time_value:BarRestrict 3.827e-05 2.191e-05 1.747 0.0812 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02135 on 673 degrees of freedom
## Multiple R-squared: 0.1797, Adjusted R-squared: 0.176
## F-statistic: 49.13 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047705 -0.018116 0.002821 0.017686 0.045161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.861e-01 1.541e-01 -5.749 1.36e-08 ***
## time_value 5.214e-05 8.508e-06 6.128 1.51e-09 ***
## BarRestrict -9.543e-01 4.260e-01 -2.240 0.0254 *
## time_value:BarRestrict 4.992e-05 2.314e-05 2.157 0.0313 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02267 on 673 degrees of freedom
## Multiple R-squared: 0.1622, Adjusted R-squared: 0.1584
## F-statistic: 43.42 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035792 -0.021598 0.000209 0.015340 0.059620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.952e-02 8.455e-02 0.467 0.640
## time_value 7.243e-07 4.640e-06 0.156 0.876
## BarRestrict -4.032e-01 2.987e+00 -0.135 0.893
## time_value:BarRestrict 2.094e-05 1.625e-04 0.129 0.898
##
## Residual standard error: 0.02289 on 673 degrees of freedom
## Multiple R-squared: 0.04988, Adjusted R-squared: 0.04564
## F-statistic: 11.78 on 3 and 673 DF, p-value: 1.585e-07
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033939 -0.012434 0.003175 0.010746 0.039576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.683e-01 1.079e-01 -3.414 0.000678 ***
## time_value 2.311e-05 5.954e-06 3.881 0.000114 ***
## BarRestrict -2.937e-01 3.033e-01 -0.968 0.333251
## time_value:BarRestrict 1.495e-05 1.647e-05 0.908 0.364318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01597 on 673 degrees of freedom
## Multiple R-squared: 0.106, Adjusted R-squared: 0.1021
## F-statistic: 26.61 on 3 and 673 DF, p-value: 2.834e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045659 -0.015330 0.003066 0.016833 0.045006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.633e-01 1.455e-01 -5.933 4.76e-09 ***
## time_value 5.089e-05 8.031e-06 6.336 4.31e-10 ***
## BarRestrict -7.367e-01 4.091e-01 -1.801 0.0722 .
## time_value:BarRestrict 3.809e-05 2.222e-05 1.714 0.0869 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02154 on 673 degrees of freedom
## Multiple R-squared: 0.1811, Adjusted R-squared: 0.1774
## F-statistic: 49.61 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042556 -0.014530 0.002094 0.016566 0.043991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.972e-01 1.407e-01 -5.664 2.19e-08 ***
## time_value 4.727e-05 7.768e-06 6.085 1.95e-09 ***
## BarRestrict -2.575e-01 3.957e-01 -0.651 0.515
## time_value:BarRestrict 1.225e-05 2.149e-05 0.570 0.569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02084 on 673 degrees of freedom
## Multiple R-squared: 0.1617, Adjusted R-squared: 0.1579
## F-statistic: 43.26 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037606 -0.018620 0.005056 0.014748 0.032723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.610e-01 1.254e-01 -4.474 9.00e-06 ***
## time_value 3.370e-05 6.920e-06 4.871 1.39e-06 ***
## BarRestrict -1.037e+00 3.555e-01 -2.916 0.00367 **
## time_value:BarRestrict 5.514e-05 1.931e-05 2.856 0.00442 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01862 on 673 degrees of freedom
## Multiple R-squared: 0.09733, Adjusted R-squared: 0.0933
## F-statistic: 24.19 on 3 and 673 DF, p-value: 7.122e-15
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036192 -0.023699 -0.004966 0.019513 0.069966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.343e-02 9.310e-02 0.252 0.801
## time_value 1.841e-06 5.107e-06 0.360 0.719
## BarRestrict 2.793e+00 1.487e+01 0.188 0.851
## time_value:BarRestrict -1.530e-04 8.095e-04 -0.189 0.850
##
## Residual standard error: 0.02575 on 673 degrees of freedom
## Multiple R-squared: 0.01512, Adjusted R-squared: 0.01073
## F-statistic: 3.443 on 3 and 673 DF, p-value: 0.01649
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041629 -0.020979 0.003643 0.016627 0.046567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.354e-01 1.069e-01 -2.202 0.027982 *
## time_value 1.595e-05 5.888e-06 2.708 0.006939 **
## BarRestrict -1.896e+00 5.340e-01 -3.551 0.000411 ***
## time_value:BarRestrict 1.019e-04 2.898e-05 3.515 0.000469 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02196 on 673 degrees of freedom
## Multiple R-squared: 0.1004, Adjusted R-squared: 0.09637
## F-statistic: 25.03 on 3 and 673 DF, p-value: 2.314e-15
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043979 -0.018127 0.003147 0.014394 0.044874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.736e-01 1.423e-01 -5.435 7.67e-08 ***
## time_value 4.573e-05 7.856e-06 5.821 9.08e-09 ***
## BarRestrict -1.096e+00 4.142e-01 -2.646 0.00834 **
## time_value:BarRestrict 5.770e-05 2.249e-05 2.565 0.01052 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02136 on 673 degrees of freedom
## Multiple R-squared: 0.1704, Adjusted R-squared: 0.1667
## F-statistic: 46.09 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044234 -0.018794 0.005849 0.015499 0.041181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.238e-01 1.398e-01 -3.031 0.002534 **
## time_value 2.660e-05 7.718e-06 3.447 0.000603 ***
## BarRestrict -9.180e-01 4.000e-01 -2.295 0.022031 *
## time_value:BarRestrict 4.866e-05 2.172e-05 2.240 0.025402 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02084 on 673 degrees of freedom
## Multiple R-squared: 0.09115, Adjusted R-squared: 0.0871
## F-statistic: 22.5 on 3 and 673 DF, p-value: 6.836e-14
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042912 -0.017487 0.004461 0.015734 0.044042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.710e-01 1.409e-01 -4.052 5.67e-05 ***
## time_value 3.463e-05 7.775e-06 4.453 9.91e-06 ***
## BarRestrict -6.989e-01 4.247e-01 -1.646 0.100
## time_value:BarRestrict 3.632e-05 2.305e-05 1.575 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02143 on 673 degrees of freedom
## Multiple R-squared: 0.1539, Adjusted R-squared: 0.1501
## F-statistic: 40.81 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041099 -0.013628 0.001977 0.013091 0.049719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.828e-01 1.268e-01 -6.960 8.07e-12 ***
## time_value 5.201e-05 7.000e-06 7.430 3.31e-13 ***
## BarRestrict -3.547e-01 3.566e-01 -0.995 0.320
## time_value:BarRestrict 1.764e-05 1.937e-05 0.911 0.363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01878 on 673 degrees of freedom
## Multiple R-squared: 0.1604, Adjusted R-squared: 0.1567
## F-statistic: 42.87 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037408 -0.012929 0.003457 0.012421 0.037585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.605e-01 1.182e-01 -3.050 0.002375 **
## time_value 2.278e-05 6.523e-06 3.493 0.000509 ***
## BarRestrict -5.621e-01 3.294e-01 -1.706 0.088436 .
## time_value:BarRestrict 2.932e-05 1.789e-05 1.639 0.101747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01744 on 673 degrees of freedom
## Multiple R-squared: 0.1409, Adjusted R-squared: 0.137
## F-statistic: 36.78 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045042 -0.026365 -0.002078 0.016772 0.072723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.536e-01 1.075e-01 3.291 0.00105 **
## time_value -1.608e-05 5.896e-06 -2.728 0.00654 **
## BarRestrict 1.837e+00 4.227e+00 0.435 0.66401
## time_value:BarRestrict -1.011e-04 2.301e-04 -0.439 0.66050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02933 on 673 degrees of freedom
## Multiple R-squared: 0.05567, Adjusted R-squared: 0.05146
## F-statistic: 13.22 on 3 and 673 DF, p-value: 2.136e-08
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044452 -0.016753 0.003098 0.014758 0.042602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.554e-01 1.346e-01 -7.097 3.24e-12 ***
## time_value 5.583e-05 7.430e-06 7.514 1.83e-13 ***
## BarRestrict -5.516e-01 3.817e-01 -1.445 0.149
## time_value:BarRestrict 2.825e-05 2.073e-05 1.362 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02 on 673 degrees of freedom
## Multiple R-squared: 0.157, Adjusted R-squared: 0.1532
## F-statistic: 41.77 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037044 -0.015010 0.001662 0.013544 0.038762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.189e-01 1.103e-01 -4.705 3.08e-06 ***
## time_value 3.146e-05 6.086e-06 5.169 3.10e-07 ***
## BarRestrict -1.173e+00 3.154e-01 -3.718 0.000217 ***
## time_value:BarRestrict 6.267e-05 1.713e-05 3.659 0.000273 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01644 on 673 degrees of freedom
## Multiple R-squared: 0.1027, Adjusted R-squared: 0.09869
## F-statistic: 25.67 on 3 and 673 DF, p-value: 9.849e-16Restaurant Restriction
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*RestaurantRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.024157 -0.009866 -0.000590 0.008085 0.049688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.094e-01 4.912e-02 -4.263 2.30e-05 ***
## time_value 1.424e-05 2.696e-06 5.283 1.72e-07 ***
## RestaurantRestrict -4.531e-01 1.584e+00 -0.286 0.775
## time_value:RestaurantRestrict 2.431e-05 8.620e-05 0.282 0.778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01333 on 673 degrees of freedom
## Multiple R-squared: 0.04844, Adjusted R-squared: 0.04419
## F-statistic: 11.42 on 3 and 673 DF, p-value: 2.603e-07
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054702 -0.020943 0.003719 0.018625 0.059624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.120e-01 1.636e-01 -5.575 3.59e-08 ***
## time_value 5.361e-05 9.029e-06 5.937 4.65e-09 ***
## RestaurantRestrict -1.701e+00 4.679e-01 -3.636 0.000299 ***
## time_value:RestaurantRestrict 9.048e-05 2.541e-05 3.561 0.000395 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02438 on 673 degrees of freedom
## Multiple R-squared: 0.1455, Adjusted R-squared: 0.1417
## F-statistic: 38.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044473 -0.020696 0.004237 0.017885 0.046803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.696e-01 1.504e-01 -5.117 4.06e-07 ***
## time_value 4.558e-05 8.301e-06 5.490 5.69e-08 ***
## RestaurantRestrict -1.442e+00 4.302e-01 -3.352 0.000848 ***
## time_value:RestaurantRestrict 7.668e-05 2.336e-05 3.282 0.001082 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02242 on 673 degrees of freedom
## Multiple R-squared: 0.128, Adjusted R-squared: 0.1241
## F-statistic: 32.92 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033596 -0.013210 0.002789 0.011723 0.044827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.358e-01 1.019e-01 -3.295 0.001036 **
## time_value 2.129e-05 5.623e-06 3.786 0.000167 ***
## RestaurantRestrict -7.585e-01 3.242e-01 -2.340 0.019580 *
## time_value:RestaurantRestrict 4.020e-05 1.759e-05 2.285 0.022607 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01581 on 673 degrees of freedom
## Multiple R-squared: 0.1034, Adjusted R-squared: 0.0994
## F-statistic: 25.87 on 3 and 673 DF, p-value: 7.575e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034534 -0.012354 0.003733 0.011387 0.028814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.068e-01 1.037e-01 -2.960 0.003187 **
## time_value 1.976e-05 5.722e-06 3.454 0.000587 ***
## RestaurantRestrict -5.610e-01 2.890e-01 -1.941 0.052654 .
## time_value:RestaurantRestrict 2.965e-05 1.570e-05 1.889 0.059277 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0153 on 673 degrees of freedom
## Multiple R-squared: 0.07596, Adjusted R-squared: 0.07184
## F-statistic: 18.44 on 3 and 673 DF, p-value: 1.657e-11
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041660 -0.014137 0.003602 0.014433 0.041623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.753e-01 1.298e-01 -4.433 1.09e-05 ***
## time_value 3.497e-05 7.163e-06 4.881 1.32e-06 ***
## RestaurantRestrict -7.953e-01 3.649e-01 -2.180 0.0296 *
## time_value:RestaurantRestrict 4.175e-05 1.982e-05 2.107 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01922 on 673 degrees of freedom
## Multiple R-squared: 0.1405, Adjusted R-squared: 0.1366
## F-statistic: 36.66 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050213 -0.017160 0.002498 0.019499 0.050938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.618e-01 1.689e-01 -4.509 7.67e-06 ***
## time_value 4.557e-05 9.325e-06 4.887 1.28e-06 ***
## RestaurantRestrict -7.121e-01 4.710e-01 -1.512 0.131
## time_value:RestaurantRestrict 3.655e-05 2.558e-05 1.429 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02493 on 673 degrees of freedom
## Multiple R-squared: 0.1889, Adjusted R-squared: 0.1853
## F-statistic: 52.24 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035311 -0.007202 0.001442 0.010093 0.041163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.560e-01 1.125e-01 -6.720 3.86e-11 ***
## time_value 4.462e-05 6.209e-06 7.185 1.78e-12 ***
## RestaurantRestrict 1.878e-02 3.136e-01 0.060 0.952
## time_value:RestaurantRestrict -2.345e-06 1.703e-05 -0.138 0.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0166 on 673 degrees of freedom
## Multiple R-squared: 0.1381, Adjusted R-squared: 0.1343
## F-statistic: 35.96 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042956 -0.014172 0.003767 0.014745 0.042128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.406e-01 1.381e-01 -5.362 1.13e-07 ***
## time_value 4.400e-05 7.624e-06 5.771 1.20e-08 ***
## RestaurantRestrict -1.975e-01 3.851e-01 -0.513 0.608
## time_value:RestaurantRestrict 8.980e-06 2.091e-05 0.429 0.668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02038 on 673 degrees of freedom
## Multiple R-squared: 0.1786, Adjusted R-squared: 0.1749
## F-statistic: 48.77 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037218 -0.017960 0.005048 0.012582 0.035991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.844e-01 8.722e-02 -3.260 0.001168 **
## time_value 1.841e-05 4.804e-06 3.832 0.000139 ***
## RestaurantRestrict -1.140e+00 4.453e-01 -2.561 0.010650 *
## time_value:RestaurantRestrict 6.099e-05 2.417e-05 2.523 0.011861 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01834 on 673 degrees of freedom
## Multiple R-squared: 0.1025, Adjusted R-squared: 0.09853
## F-statistic: 25.63 on 3 and 673 DF, p-value: 1.044e-15
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.039321 -0.021466 0.005076 0.016554 0.042570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.738e-01 1.388e-01 -2.693 0.007250 **
## time_value 2.346e-05 7.657e-06 3.064 0.002269 **
## RestaurantRestrict -1.765e+00 4.537e-01 -3.891 0.000110 ***
## time_value:RestaurantRestrict 9.461e-05 2.462e-05 3.844 0.000133 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02174 on 673 degrees of freedom
## Multiple R-squared: 0.0934, Adjusted R-squared: 0.08935
## F-statistic: 23.11 on 3 and 673 DF, p-value: 3.012e-14
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0122371 -0.0037473 0.0004605 0.0036079 0.0158151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.548e-01 3.472e-02 -7.338 6.28e-13 ***
## time_value 1.558e-05 1.917e-06 8.129 2.09e-15 ***
## RestaurantRestrict -1.156e-01 9.762e-02 -1.184 0.237
## time_value:RestaurantRestrict 6.040e-06 5.302e-06 1.139 0.255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.005141 on 673 degrees of freedom
## Multiple R-squared: 0.1216, Adjusted R-squared: 0.1177
## F-statistic: 31.07 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060955 -0.025076 0.003689 0.024380 0.061472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.298e+00 2.020e-01 -6.424 2.52e-10 ***
## time_value 7.542e-05 1.115e-05 6.765 2.91e-11 ***
## RestaurantRestrict -1.746e+00 5.679e-01 -3.075 0.00219 **
## time_value:RestaurantRestrict 9.207e-05 3.085e-05 2.985 0.00294 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02991 on 673 degrees of freedom
## Multiple R-squared: 0.1918, Adjusted R-squared: 0.1882
## F-statistic: 53.24 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.039247 -0.016400 0.003993 0.013700 0.039325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.805e-01 1.170e-01 -4.106 4.52e-05 ***
## time_value 2.945e-05 6.457e-06 4.561 6.04e-06 ***
## RestaurantRestrict -1.025e+00 3.527e-01 -2.905 0.00379 **
## time_value:RestaurantRestrict 5.462e-05 1.914e-05 2.853 0.00446 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01779 on 673 degrees of freedom
## Multiple R-squared: 0.08493, Adjusted R-squared: 0.08085
## F-statistic: 20.82 on 3 and 673 DF, p-value: 6.555e-13
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053548 -0.016150 0.002711 0.016111 0.057910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.152e+00 1.699e-01 -6.780 2.64e-11 ***
## time_value 6.711e-05 9.376e-06 7.158 2.15e-12 ***
## RestaurantRestrict -2.565e-01 4.735e-01 -0.542 0.588
## time_value:RestaurantRestrict 1.133e-05 2.572e-05 0.441 0.660
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02506 on 673 degrees of freedom
## Multiple R-squared: 0.2379, Adjusted R-squared: 0.2345
## F-statistic: 70.02 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042840 -0.018423 0.004236 0.016388 0.039710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.752e-01 1.382e-01 -5.608 3.0e-08 ***
## time_value 4.575e-05 7.631e-06 5.996 3.3e-09 ***
## RestaurantRestrict -1.043e+00 3.854e-01 -2.707 0.00696 **
## time_value:RestaurantRestrict 5.505e-05 2.093e-05 2.630 0.00874 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0204 on 673 degrees of freedom
## Multiple R-squared: 0.1451, Adjusted R-squared: 0.1413
## F-statistic: 38.07 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042767 -0.024601 -0.003248 0.018805 0.066957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.501e-01 9.826e-02 1.528 0.127
## time_value -5.110e-06 5.392e-06 -0.948 0.344
## RestaurantRestrict -8.969e-01 3.850e+00 -0.233 0.816
## time_value:RestaurantRestrict 4.761e-05 2.095e-04 0.227 0.820
##
## Residual standard error: 0.0267 on 673 degrees of freedom
## Multiple R-squared: 0.05514, Adjusted R-squared: 0.05093
## F-statistic: 13.09 on 3 and 673 DF, p-value: 2.56e-08
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048063 -0.018784 0.003916 0.018701 0.045893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.321e-01 1.563e-01 -5.965 3.96e-09 ***
## time_value 5.462e-05 8.626e-06 6.333 4.41e-10 ***
## RestaurantRestrict -5.102e-01 4.356e-01 -1.171 0.242
## time_value:RestaurantRestrict 2.577e-05 2.366e-05 1.089 0.277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02306 on 673 degrees of freedom
## Multiple R-squared: 0.1599, Adjusted R-squared: 0.1561
## F-statistic: 42.68 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040005 -0.017859 0.003537 0.015898 0.043926
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.795e-01 1.372e-01 -6.409 2.76e-10 ***
## time_value 5.169e-05 7.575e-06 6.824 1.97e-11 ***
## RestaurantRestrict -1.129e+00 3.858e-01 -2.926 0.00354 **
## time_value:RestaurantRestrict 5.968e-05 2.096e-05 2.848 0.00453 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02032 on 673 degrees of freedom
## Multiple R-squared: 0.1485, Adjusted R-squared: 0.1447
## F-statistic: 39.13 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045112 -0.014374 0.001635 0.017129 0.046951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.756e-01 1.525e-01 -4.429 1.10e-05 ***
## time_value 4.074e-05 8.418e-06 4.840 1.61e-06 ***
## RestaurantRestrict -5.262e-01 4.288e-01 -1.227 0.220
## time_value:RestaurantRestrict 2.669e-05 2.329e-05 1.146 0.252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02258 on 673 degrees of freedom
## Multiple R-squared: 0.1841, Adjusted R-squared: 0.1805
## F-statistic: 50.63 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046350 -0.013070 0.003793 0.015348 0.042190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.867e-01 1.505e-01 -5.227 2.30e-07 ***
## time_value 4.670e-05 8.307e-06 5.622 2.77e-08 ***
## RestaurantRestrict -1.949e-01 4.196e-01 -0.464 0.642
## time_value:RestaurantRestrict 8.567e-06 2.279e-05 0.376 0.707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02221 on 673 degrees of freedom
## Multiple R-squared: 0.2066, Adjusted R-squared: 0.2031
## F-statistic: 58.41 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038021 -0.016073 0.001985 0.014755 0.047114
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.187e-01 1.282e-01 -4.827 1.71e-06 ***
## time_value 3.734e-05 7.074e-06 5.279 1.75e-07 ***
## RestaurantRestrict -4.529e-01 3.634e-01 -1.246 0.213
## time_value:RestaurantRestrict 2.320e-05 1.974e-05 1.175 0.240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01904 on 673 degrees of freedom
## Multiple R-squared: 0.1308, Adjusted R-squared: 0.127
## F-statistic: 33.77 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038587 -0.014453 0.001388 0.014769 0.045932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.134e-01 1.006e-01 -3.116 0.001912 **
## time_value 2.032e-05 5.542e-06 3.668 0.000264 ***
## RestaurantRestrict -1.228e+00 4.676e-01 -2.627 0.008807 **
## time_value:RestaurantRestrict 6.548e-05 2.538e-05 2.580 0.010091 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02037 on 673 degrees of freedom
## Multiple R-squared: 0.1354, Adjusted R-squared: 0.1315
## F-statistic: 35.12 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05225 -0.01708 0.00211 0.01805 0.05508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.140e+00 1.732e-01 -6.583 9.30e-11 ***
## time_value 6.652e-05 9.557e-06 6.960 8.09e-12 ***
## RestaurantRestrict -1.723e-01 4.868e-01 -0.354 0.723
## time_value:RestaurantRestrict 6.741e-06 2.644e-05 0.255 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02564 on 673 degrees of freedom
## Multiple R-squared: 0.2357, Adjusted R-squared: 0.2323
## F-statistic: 69.18 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05447 -0.02198 0.00383 0.01987 0.05586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.471e-01 1.723e-01 -5.495 5.54e-08 ***
## time_value 5.578e-05 9.511e-06 5.865 7.05e-09 ***
## RestaurantRestrict -1.580e+00 5.104e-01 -3.095 0.00205 **
## time_value:RestaurantRestrict 8.359e-05 2.771e-05 3.017 0.00265 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02603 on 673 degrees of freedom
## Multiple R-squared: 0.1658, Adjusted R-squared: 0.1621
## F-statistic: 44.58 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043422 -0.021264 0.004372 0.017756 0.045205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.196e-01 1.470e-01 -4.216 2.82e-05 ***
## time_value 3.727e-05 8.110e-06 4.595 5.17e-06 ***
## RestaurantRestrict -1.793e+00 4.391e-01 -4.084 4.97e-05 ***
## time_value:RestaurantRestrict 9.596e-05 2.384e-05 4.026 6.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02227 on 673 degrees of freedom
## Multiple R-squared: 0.1071, Adjusted R-squared: 0.1031
## F-statistic: 26.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036836 -0.015069 0.002754 0.013072 0.038411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.689e-01 1.138e-01 -5.001 7.29e-07 ***
## time_value 3.461e-05 6.279e-06 5.512 5.07e-08 ***
## RestaurantRestrict -6.767e-01 3.282e-01 -2.062 0.0396 *
## time_value:RestaurantRestrict 3.570e-05 1.782e-05 2.003 0.0456 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01701 on 673 degrees of freedom
## Multiple R-squared: 0.09105, Adjusted R-squared: 0.087
## F-statistic: 22.47 on 3 and 673 DF, p-value: 7.102e-14
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.039575 -0.017283 0.004611 0.014811 0.039518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.573e-01 1.313e-01 -4.246 2.49e-05 ***
## time_value 3.354e-05 7.244e-06 4.630 4.38e-06 ***
## RestaurantRestrict -6.680e-01 3.690e-01 -1.810 0.0707 .
## time_value:RestaurantRestrict 3.501e-05 2.004e-05 1.747 0.0811 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01943 on 673 degrees of freedom
## Multiple R-squared: 0.1056, Adjusted R-squared: 0.1016
## F-statistic: 26.49 on 3 and 673 DF, p-value: 3.319e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040950 -0.025181 -0.005945 0.019199 0.076035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.955e-01 1.047e-01 1.867 0.0624 .
## time_value -7.459e-06 5.747e-06 -1.298 0.1947
## RestaurantRestrict 7.618e-01 3.538e+00 0.215 0.8296
## time_value:RestaurantRestrict -4.254e-05 1.926e-04 -0.221 0.8252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02845 on 673 degrees of freedom
## Multiple R-squared: 0.04647, Adjusted R-squared: 0.04222
## F-statistic: 10.93 on 3 and 673 DF, p-value: 5.116e-07
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055302 -0.025777 0.001748 0.021831 0.067621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.638e-01 1.306e-01 -3.550 0.000412 ***
## time_value 2.911e-05 7.190e-06 4.049 5.75e-05 ***
## RestaurantRestrict -2.625e+00 8.918e-01 -2.943 0.003363 **
## time_value:RestaurantRestrict 1.408e-04 4.839e-05 2.909 0.003747 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02928 on 673 degrees of freedom
## Multiple R-squared: 0.1273, Adjusted R-squared: 0.1234
## F-statistic: 32.73 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043611 -0.016199 0.001539 0.017494 0.060825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.458e-01 1.080e-01 1.351 0.177
## time_value -4.627e-06 5.939e-06 -0.779 0.436
## RestaurantRestrict -6.511e-01 8.246e-01 -0.790 0.430
## time_value:RestaurantRestrict 3.417e-05 4.479e-05 0.763 0.446
##
## Residual standard error: 0.02569 on 673 degrees of freedom
## Multiple R-squared: 0.1272, Adjusted R-squared: 0.1233
## F-statistic: 32.69 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053596 -0.015873 0.002651 0.021972 0.054609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.736e-01 1.842e-01 -4.199 3.04e-05 ***
## time_value 4.637e-05 1.017e-05 4.560 6.08e-06 ***
## RestaurantRestrict -6.711e-01 5.135e-01 -1.307 0.192
## time_value:RestaurantRestrict 3.401e-05 2.789e-05 1.219 0.223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02718 on 673 degrees of freedom
## Multiple R-squared: 0.2204, Adjusted R-squared: 0.2169
## F-statistic: 63.41 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.028080 -0.009430 0.002061 0.008308 0.029982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.615e-01 8.048e-02 -5.735 1.48e-08 ***
## time_value 2.819e-05 4.442e-06 6.347 4.03e-10 ***
## RestaurantRestrict -2.362e-01 2.302e-01 -1.026 0.305
## time_value:RestaurantRestrict 1.202e-05 1.250e-05 0.962 0.336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.012 on 673 degrees of freedom
## Multiple R-squared: 0.1017, Adjusted R-squared: 0.09773
## F-statistic: 25.41 on 3 and 673 DF, p-value: 1.402e-15
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.029430 -0.011550 0.003415 0.009343 0.034449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.340e-01 8.775e-02 -4.946 9.59e-07 ***
## time_value 2.647e-05 4.843e-06 5.466 6.50e-08 ***
## RestaurantRestrict -6.631e-01 2.553e-01 -2.597 0.00962 **
## time_value:RestaurantRestrict 3.512e-05 1.387e-05 2.533 0.01153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01317 on 673 degrees of freedom
## Multiple R-squared: 0.1091, Adjusted R-squared: 0.1052
## F-statistic: 27.49 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042965 -0.013417 0.001414 0.016665 0.050083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.822e-01 1.447e-01 -5.405 9.01e-08 ***
## time_value 4.651e-05 7.988e-06 5.822 8.99e-09 ***
## RestaurantRestrict -7.390e-01 4.034e-01 -1.832 0.0674 .
## time_value:RestaurantRestrict 3.827e-05 2.191e-05 1.747 0.0812 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02135 on 673 degrees of freedom
## Multiple R-squared: 0.1797, Adjusted R-squared: 0.176
## F-statistic: 49.13 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047705 -0.018116 0.002821 0.017686 0.045161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.861e-01 1.541e-01 -5.749 1.36e-08 ***
## time_value 5.214e-05 8.508e-06 6.128 1.51e-09 ***
## RestaurantRestrict -9.543e-01 4.260e-01 -2.240 0.0254 *
## time_value:RestaurantRestrict 4.992e-05 2.314e-05 2.157 0.0313 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02267 on 673 degrees of freedom
## Multiple R-squared: 0.1622, Adjusted R-squared: 0.1584
## F-statistic: 43.42 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035792 -0.021598 0.000209 0.015340 0.059620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.952e-02 8.455e-02 0.467 0.640
## time_value 7.243e-07 4.640e-06 0.156 0.876
## RestaurantRestrict -4.032e-01 2.987e+00 -0.135 0.893
## time_value:RestaurantRestrict 2.094e-05 1.625e-04 0.129 0.898
##
## Residual standard error: 0.02289 on 673 degrees of freedom
## Multiple R-squared: 0.04988, Adjusted R-squared: 0.04564
## F-statistic: 11.78 on 3 and 673 DF, p-value: 1.585e-07
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033939 -0.012434 0.003175 0.010746 0.039576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.683e-01 1.079e-01 -3.414 0.000678 ***
## time_value 2.311e-05 5.954e-06 3.881 0.000114 ***
## RestaurantRestrict -2.937e-01 3.033e-01 -0.968 0.333251
## time_value:RestaurantRestrict 1.495e-05 1.647e-05 0.908 0.364318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01597 on 673 degrees of freedom
## Multiple R-squared: 0.106, Adjusted R-squared: 0.1021
## F-statistic: 26.61 on 3 and 673 DF, p-value: 2.834e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045659 -0.015330 0.003066 0.016833 0.045006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.633e-01 1.455e-01 -5.933 4.76e-09 ***
## time_value 5.089e-05 8.031e-06 6.336 4.31e-10 ***
## RestaurantRestrict -7.367e-01 4.091e-01 -1.801 0.0722 .
## time_value:RestaurantRestrict 3.809e-05 2.222e-05 1.714 0.0869 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02154 on 673 degrees of freedom
## Multiple R-squared: 0.1811, Adjusted R-squared: 0.1774
## F-statistic: 49.61 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042556 -0.014530 0.002094 0.016566 0.043991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.972e-01 1.407e-01 -5.664 2.19e-08 ***
## time_value 4.727e-05 7.768e-06 6.085 1.95e-09 ***
## RestaurantRestrict -2.575e-01 3.957e-01 -0.651 0.515
## time_value:RestaurantRestrict 1.225e-05 2.149e-05 0.570 0.569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02084 on 673 degrees of freedom
## Multiple R-squared: 0.1617, Adjusted R-squared: 0.1579
## F-statistic: 43.26 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037146 -0.018824 0.004524 0.014192 0.035077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.805e-01 9.251e-02 -3.033 0.002518 **
## time_value 1.819e-05 5.096e-06 3.570 0.000383 ***
## RestaurantRestrict -1.400e+00 4.373e-01 -3.202 0.001428 **
## time_value:RestaurantRestrict 7.519e-05 2.373e-05 3.168 0.001605 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01879 on 673 degrees of freedom
## Multiple R-squared: 0.08172, Adjusted R-squared: 0.07762
## F-statistic: 19.96 on 3 and 673 DF, p-value: 2.097e-12
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036192 -0.023699 -0.004966 0.019513 0.069966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.343e-02 9.310e-02 0.252 0.801
## time_value 1.841e-06 5.107e-06 0.360 0.719
## RestaurantRestrict 2.793e+00 1.487e+01 0.188 0.851
## time_value:RestaurantRestrict -1.530e-04 8.095e-04 -0.189 0.850
##
## Residual standard error: 0.02575 on 673 degrees of freedom
## Multiple R-squared: 0.01512, Adjusted R-squared: 0.01073
## F-statistic: 3.443 on 3 and 673 DF, p-value: 0.01649
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03749 -0.02423 0.00203 0.01603 0.05119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.163e-01 8.313e-02 1.399 0.162
## time_value -3.538e-06 4.562e-06 -0.775 0.438
## RestaurantRestrict -8.467e-01 3.021e+00 -0.280 0.779
## time_value:RestaurantRestrict 4.517e-05 1.644e-04 0.275 0.784
##
## Residual standard error: 0.0226 on 673 degrees of freedom
## Multiple R-squared: 0.04737, Adjusted R-squared: 0.04313
## F-statistic: 11.16 on 3 and 673 DF, p-value: 3.752e-07
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043979 -0.018127 0.003147 0.014394 0.044874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.736e-01 1.423e-01 -5.435 7.67e-08 ***
## time_value 4.573e-05 7.856e-06 5.821 9.08e-09 ***
## RestaurantRestrict -1.096e+00 4.142e-01 -2.646 0.00834 **
## time_value:RestaurantRestrict 5.770e-05 2.249e-05 2.565 0.01052 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02136 on 673 degrees of freedom
## Multiple R-squared: 0.1704, Adjusted R-squared: 0.1667
## F-statistic: 46.09 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044234 -0.018794 0.005849 0.015499 0.041181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.238e-01 1.398e-01 -3.031 0.002534 **
## time_value 2.660e-05 7.718e-06 3.447 0.000603 ***
## RestaurantRestrict -9.180e-01 4.000e-01 -2.295 0.022031 *
## time_value:RestaurantRestrict 4.866e-05 2.172e-05 2.240 0.025402 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02084 on 673 degrees of freedom
## Multiple R-squared: 0.09115, Adjusted R-squared: 0.0871
## F-statistic: 22.5 on 3 and 673 DF, p-value: 6.836e-14
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044464 -0.015552 0.004332 0.015928 0.043039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.835e-01 1.436e-01 -4.761 2.36e-06 ***
## time_value 4.086e-05 7.924e-06 5.157 3.31e-07 ***
## RestaurantRestrict -4.650e-01 4.036e-01 -1.152 0.250
## time_value:RestaurantRestrict 2.352e-05 2.192e-05 1.073 0.284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02126 on 673 degrees of freedom
## Multiple R-squared: 0.1674, Adjusted R-squared: 0.1637
## F-statistic: 45.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041099 -0.013628 0.001977 0.013091 0.049719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.828e-01 1.268e-01 -6.960 8.07e-12 ***
## time_value 5.201e-05 7.000e-06 7.430 3.31e-13 ***
## RestaurantRestrict -3.547e-01 3.566e-01 -0.995 0.320
## time_value:RestaurantRestrict 1.764e-05 1.937e-05 0.911 0.363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01878 on 673 degrees of freedom
## Multiple R-squared: 0.1604, Adjusted R-squared: 0.1567
## F-statistic: 42.87 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037408 -0.012929 0.003457 0.012421 0.037585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.605e-01 1.182e-01 -3.050 0.002375 **
## time_value 2.278e-05 6.523e-06 3.493 0.000509 ***
## RestaurantRestrict -5.621e-01 3.294e-01 -1.706 0.088436 .
## time_value:RestaurantRestrict 2.932e-05 1.789e-05 1.639 0.101747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01744 on 673 degrees of freedom
## Multiple R-squared: 0.1409, Adjusted R-squared: 0.137
## F-statistic: 36.78 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045042 -0.026365 -0.002078 0.016772 0.072723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.536e-01 1.075e-01 3.291 0.00105 **
## time_value -1.608e-05 5.896e-06 -2.728 0.00654 **
## RestaurantRestrict 1.837e+00 4.227e+00 0.435 0.66401
## time_value:RestaurantRestrict -1.011e-04 2.301e-04 -0.439 0.66050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02933 on 673 degrees of freedom
## Multiple R-squared: 0.05567, Adjusted R-squared: 0.05146
## F-statistic: 13.22 on 3 and 673 DF, p-value: 2.136e-08
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044452 -0.016753 0.003098 0.014758 0.042602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.554e-01 1.346e-01 -7.097 3.24e-12 ***
## time_value 5.583e-05 7.430e-06 7.514 1.83e-13 ***
## RestaurantRestrict -5.516e-01 3.817e-01 -1.445 0.149
## time_value:RestaurantRestrict 2.825e-05 2.073e-05 1.362 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02 on 673 degrees of freedom
## Multiple R-squared: 0.157, Adjusted R-squared: 0.1532
## F-statistic: 41.77 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037044 -0.015010 0.001662 0.013544 0.038762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.189e-01 1.103e-01 -4.705 3.08e-06 ***
## time_value 3.146e-05 6.086e-06 5.169 3.10e-07 ***
## RestaurantRestrict -1.173e+00 3.154e-01 -3.718 0.000217 ***
## time_value:RestaurantRestrict 6.267e-05 1.713e-05 3.659 0.000273 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01644 on 673 degrees of freedom
## Multiple R-squared: 0.1027, Adjusted R-squared: 0.09869
## F-statistic: 25.67 on 3 and 673 DF, p-value: 9.849e-16Completely staying at home
Emergency Declaration
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*EmergDec,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.118181 -0.025234 -0.003872 0.021509 0.143624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.333e+00 2.529e-01 21.084 <2e-16 ***
## time_value -2.768e-04 1.396e-05 -19.823 <2e-16 ***
## EmergDec 8.722e-01 6.760e-01 1.290 0.197
## time_value:EmergDec -4.329e-05 3.673e-05 -1.178 0.239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03669 on 673 degrees of freedom
## Multiple R-squared: 0.44, Adjusted R-squared: 0.4375
## F-statistic: 176.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072783 -0.022718 -0.006026 0.017032 0.157695
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.011e+00 2.225e-01 18.029 < 2e-16 ***
## time_value -2.077e-04 1.228e-05 -16.911 < 2e-16 ***
## EmergDec 2.630e+00 6.046e-01 4.349 1.58e-05 ***
## time_value:EmergDec -1.389e-04 3.285e-05 -4.228 2.68e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03249 on 673 degrees of freedom
## Multiple R-squared: 0.3826, Adjusted R-squared: 0.3799
## F-statistic: 139 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.094329 -0.022900 -0.006559 0.016189 0.135311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.201e+00 2.246e-01 18.708 < 2e-16 ***
## time_value -2.177e-04 1.240e-05 -17.557 < 2e-16 ***
## EmergDec 2.967e+00 6.003e-01 4.943 9.72e-07 ***
## time_value:EmergDec -1.572e-04 3.261e-05 -4.820 1.78e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03258 on 673 degrees of freedom
## Multiple R-squared: 0.4112, Adjusted R-squared: 0.4086
## F-statistic: 156.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12424 -0.02130 -0.00306 0.01671 0.10489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.796e+00 2.203e-01 21.773 < 2e-16 ***
## time_value -2.472e-04 1.216e-05 -20.327 < 2e-16 ***
## EmergDec 1.901e+00 5.888e-01 3.228 0.00131 **
## time_value:EmergDec -9.746e-05 3.199e-05 -3.047 0.00240 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03196 on 673 degrees of freedom
## Multiple R-squared: 0.472, Adjusted R-squared: 0.4697
## F-statistic: 200.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.209766 -0.019353 -0.004193 0.019150 0.137197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.999e+00 2.607e-01 19.177 < 2e-16 ***
## time_value -2.595e-04 1.439e-05 -18.034 < 2e-16 ***
## EmergDec 2.851e+00 6.580e-01 4.334 1.69e-05 ***
## time_value:EmergDec -1.463e-04 3.577e-05 -4.091 4.81e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03692 on 673 degrees of freedom
## Multiple R-squared: 0.5849, Adjusted R-squared: 0.583
## F-statistic: 316.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140846 -0.024861 -0.007512 0.020798 0.170492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.947e+00 2.580e-01 19.173 < 2e-16 ***
## time_value -2.558e-04 1.424e-05 -17.955 < 2e-16 ***
## EmergDec 5.680e+00 6.897e-01 8.235 9.35e-16 ***
## time_value:EmergDec -3.023e-04 3.747e-05 -8.067 3.32e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03744 on 673 degrees of freedom
## Multiple R-squared: 0.4738, Adjusted R-squared: 0.4715
## F-statistic: 202 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.200220 -0.026513 -0.008043 0.020840 0.216985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.862e+00 2.825e-01 17.21 <2e-16 ***
## time_value -2.521e-04 1.559e-05 -16.17 <2e-16 ***
## EmergDec 8.068e+00 7.488e-01 10.77 <2e-16 ***
## time_value:EmergDec -4.304e-04 4.069e-05 -10.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04084 on 673 degrees of freedom
## Multiple R-squared: 0.5273, Adjusted R-squared: 0.5252
## F-statistic: 250.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.187824 -0.021491 -0.004255 0.019569 0.169197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.736e+00 2.415e-01 11.33 <2e-16 ***
## time_value -1.334e-04 1.333e-05 -10.00 <2e-16 ***
## EmergDec 7.657e+00 6.456e-01 11.86 <2e-16 ***
## time_value:EmergDec -4.081e-04 3.508e-05 -11.63 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03504 on 673 degrees of freedom
## Multiple R-squared: 0.6444, Adjusted R-squared: 0.6428
## F-statistic: 406.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.093040 -0.024825 -0.006167 0.016593 0.166592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.943e+00 2.424e-01 20.387 <2e-16 ***
## time_value -2.554e-04 1.338e-05 -19.086 <2e-16 ***
## EmergDec 5.941e+00 6.590e-01 9.017 <2e-16 ***
## time_value:EmergDec -3.158e-04 3.580e-05 -8.822 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03541 on 673 degrees of freedom
## Multiple R-squared: 0.5268, Adjusted R-squared: 0.5247
## F-statistic: 249.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.162729 -0.020582 -0.003494 0.017246 0.129487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.667e+00 2.476e-01 14.810 < 2e-16 ***
## time_value -1.864e-04 1.367e-05 -13.636 < 2e-16 ***
## EmergDec 4.648e+00 6.509e-01 7.140 2.43e-12 ***
## time_value:EmergDec -2.468e-04 3.537e-05 -6.978 7.20e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03568 on 673 degrees of freedom
## Multiple R-squared: 0.4131, Adjusted R-squared: 0.4105
## F-statistic: 157.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068552 -0.020153 -0.005537 0.016502 0.142500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.045e+00 2.141e-01 18.891 < 2e-16 ***
## time_value -2.084e-04 1.182e-05 -17.632 < 2e-16 ***
## EmergDec 4.967e+00 5.868e-01 8.464 < 2e-16 ***
## time_value:EmergDec -2.642e-04 3.188e-05 -8.290 6.15e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03138 on 673 degrees of freedom
## Multiple R-squared: 0.4814, Adjusted R-squared: 0.479
## F-statistic: 208.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.167423 -0.019881 -0.002859 0.019213 0.155331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.655e+00 2.506e-01 18.576 <2e-16 ***
## time_value -2.421e-04 1.384e-05 -17.498 <2e-16 ***
## EmergDec -1.479e+00 6.326e-01 -2.338 0.0197 *
## time_value:EmergDec 8.757e-05 3.439e-05 2.547 0.0111 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03549 on 673 degrees of freedom
## Multiple R-squared: 0.5115, Adjusted R-squared: 0.5093
## F-statistic: 234.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.160064 -0.026280 -0.006819 0.020351 0.176467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.581e+00 2.799e-01 19.936 < 2e-16 ***
## time_value -2.927e-04 1.545e-05 -18.939 < 2e-16 ***
## EmergDec 3.636e+00 7.360e-01 4.941 9.83e-07 ***
## time_value:EmergDec -1.923e-04 3.999e-05 -4.808 1.88e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04034 on 673 degrees of freedom
## Multiple R-squared: 0.4465, Adjusted R-squared: 0.444
## F-statistic: 180.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074713 -0.024426 -0.006103 0.016123 0.124697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.320e+00 2.331e-01 22.821 < 2e-16 ***
## time_value -2.766e-04 1.287e-05 -21.492 < 2e-16 ***
## EmergDec 1.871e+00 6.336e-01 2.953 0.00326 **
## time_value:EmergDec -9.702e-05 3.442e-05 -2.819 0.00496 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03405 on 673 degrees of freedom
## Multiple R-squared: 0.4805, Adjusted R-squared: 0.4782
## F-statistic: 207.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18714 -0.02570 -0.00585 0.02313 0.19870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.092e+00 2.648e-01 19.232 <2e-16 ***
## time_value -2.649e-04 1.462e-05 -18.125 <2e-16 ***
## EmergDec 6.117e+00 6.961e-01 8.788 <2e-16 ***
## time_value:EmergDec -3.249e-04 3.783e-05 -8.589 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03815 on 673 degrees of freedom
## Multiple R-squared: 0.5199, Adjusted R-squared: 0.5178
## F-statistic: 242.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140235 -0.024779 -0.006304 0.021323 0.150195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.908e+00 2.601e-01 18.874 < 2e-16 ***
## time_value -2.548e-04 1.436e-05 -17.750 < 2e-16 ***
## EmergDec 4.485e+00 6.671e-01 6.724 3.79e-11 ***
## time_value:EmergDec -2.387e-04 3.626e-05 -6.583 9.26e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03709 on 673 degrees of freedom
## Multiple R-squared: 0.4442, Adjusted R-squared: 0.4418
## F-statistic: 179.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.132875 -0.024683 -0.006244 0.019287 0.165602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.216e+00 2.496e-01 20.895 < 2e-16 ***
## time_value -2.722e-04 1.378e-05 -19.752 < 2e-16 ***
## EmergDec 4.626e+00 6.728e-01 6.876 1.41e-11 ***
## time_value:EmergDec -2.459e-04 3.655e-05 -6.727 3.71e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03634 on 673 degrees of freedom
## Multiple R-squared: 0.4819, Adjusted R-squared: 0.4796
## F-statistic: 208.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.132696 -0.023217 -0.004515 0.019575 0.157990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.803e+00 2.410e-01 24.080 <2e-16 ***
## time_value -3.043e-04 1.330e-05 -22.873 <2e-16 ***
## EmergDec 8.989e-01 6.182e-01 1.454 0.146
## time_value:EmergDec -4.389e-05 3.360e-05 -1.306 0.192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03436 on 673 degrees of freedom
## Multiple R-squared: 0.5012, Adjusted R-squared: 0.499
## F-statistic: 225.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.138433 -0.020795 -0.004425 0.018025 0.143471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.117e+00 2.145e-01 19.189 < 2e-16 ***
## time_value -2.125e-04 1.184e-05 -17.944 < 2e-16 ***
## EmergDec 4.354e+00 5.734e-01 7.593 1.05e-13 ***
## time_value:EmergDec -2.320e-04 3.116e-05 -7.446 2.96e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03112 on 673 degrees of freedom
## Multiple R-squared: 0.4542, Adjusted R-squared: 0.4518
## F-statistic: 186.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.222961 -0.025088 -0.005798 0.021354 0.190146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.858e+00 2.718e-01 17.872 <2e-16 ***
## time_value -2.515e-04 1.501e-05 -16.762 <2e-16 ***
## EmergDec 7.328e+00 7.206e-01 10.170 <2e-16 ***
## time_value:EmergDec -3.895e-04 3.916e-05 -9.948 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0393 on 673 degrees of freedom
## Multiple R-squared: 0.5739, Adjusted R-squared: 0.572
## F-statistic: 302.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.195948 -0.025671 -0.006125 0.020821 0.224362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.614e+00 2.996e-01 15.399 < 2e-16 ***
## time_value -2.378e-04 1.654e-05 -14.374 < 2e-16 ***
## EmergDec 4.155e+00 7.624e-01 5.450 7.09e-08 ***
## time_value:EmergDec -2.177e-04 4.144e-05 -5.254 2.00e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04258 on 673 degrees of freedom
## Multiple R-squared: 0.4854, Adjusted R-squared: 0.4831
## F-statistic: 211.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077522 -0.023275 -0.005773 0.017844 0.225133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.542e+00 2.454e-01 22.577 < 2e-16 ***
## time_value -2.880e-04 1.355e-05 -21.260 < 2e-16 ***
## EmergDec 3.441e+00 6.784e-01 5.072 5.09e-07 ***
## time_value:EmergDec -1.822e-04 3.685e-05 -4.944 9.66e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0361 on 673 degrees of freedom
## Multiple R-squared: 0.5034, Adjusted R-squared: 0.5012
## F-statistic: 227.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.200850 -0.027417 -0.007042 0.026083 0.149015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.480e+00 2.116e-01 16.45 <2e-16 ***
## time_value -1.748e-04 1.166e-05 -14.99 <2e-16 ***
## EmergDec 1.209e+01 9.364e-01 12.91 <2e-16 ***
## time_value:EmergDec -6.522e-04 5.084e-05 -12.83 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04254 on 673 degrees of freedom
## Multiple R-squared: 0.4429, Adjusted R-squared: 0.4404
## F-statistic: 178.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109147 -0.026878 -0.006463 0.020557 0.173275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.949e+00 2.675e-01 18.499 < 2e-16 ***
## time_value -2.566e-04 1.477e-05 -17.377 < 2e-16 ***
## EmergDec 5.849e+00 7.271e-01 8.044 3.94e-15 ***
## time_value:EmergDec -3.111e-04 3.950e-05 -7.876 1.36e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03908 on 673 degrees of freedom
## Multiple R-squared: 0.4646, Adjusted R-squared: 0.4622
## F-statistic: 194.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079281 -0.024099 -0.007712 0.017227 0.166329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.058e+00 2.412e-01 20.967 < 2e-16 ***
## time_value -2.634e-04 1.332e-05 -19.782 < 2e-16 ***
## EmergDec 4.293e+00 6.557e-01 6.547 1.16e-10 ***
## time_value:EmergDec -2.279e-04 3.562e-05 -6.398 2.95e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03524 on 673 degrees of freedom
## Multiple R-squared: 0.4766, Adjusted R-squared: 0.4743
## F-statistic: 204.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059928 -0.021247 -0.005491 0.015740 0.162425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.660e+00 2.114e-01 17.315 < 2e-16 ***
## time_value -1.884e-04 1.167e-05 -16.152 < 2e-16 ***
## EmergDec 3.441e+00 5.793e-01 5.939 4.60e-09 ***
## time_value:EmergDec -1.832e-04 3.147e-05 -5.822 9.01e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03098 on 673 degrees of freedom
## Multiple R-squared: 0.3875, Adjusted R-squared: 0.3847
## F-statistic: 141.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.102498 -0.025953 -0.006006 0.017715 0.126098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.887e+00 2.500e-01 19.549 <2e-16 ***
## time_value -2.525e-04 1.380e-05 -18.300 <2e-16 ***
## EmergDec 1.137e+00 6.738e-01 1.688 0.0919 .
## time_value:EmergDec -5.796e-05 3.661e-05 -1.583 0.1138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03639 on 673 degrees of freedom
## Multiple R-squared: 0.4034, Adjusted R-squared: 0.4007
## F-statistic: 151.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.132201 -0.019995 -0.004334 0.018395 0.126754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.948e+00 2.203e-01 17.923 < 2e-16 ***
## time_value -2.024e-04 1.216e-05 -16.642 < 2e-16 ***
## EmergDec 2.998e+00 5.839e-01 5.134 3.71e-07 ***
## time_value:EmergDec -1.582e-04 3.173e-05 -4.985 7.89e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03185 on 673 degrees of freedom
## Multiple R-squared: 0.399, Adjusted R-squared: 0.3963
## F-statistic: 148.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.084465 -0.029972 -0.007477 0.022218 0.169081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.956e+00 2.952e-01 16.787 < 2e-16 ***
## time_value -2.570e-04 1.630e-05 -15.771 < 2e-16 ***
## EmergDec 3.122e+00 8.023e-01 3.891 0.000110 ***
## time_value:EmergDec -1.656e-04 4.359e-05 -3.798 0.000159 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04312 on 673 degrees of freedom
## Multiple R-squared: 0.3678, Adjusted R-squared: 0.365
## F-statistic: 130.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08008 -0.02513 -0.00650 0.01985 0.18124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.900e+00 2.528e-01 23.333 < 2e-16 ***
## time_value -3.097e-04 1.396e-05 -22.191 < 2e-16 ***
## EmergDec 4.977e+00 6.872e-01 7.242 1.21e-12 ***
## time_value:EmergDec -2.647e-04 3.733e-05 -7.090 3.39e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03693 on 673 degrees of freedom
## Multiple R-squared: 0.5388, Adjusted R-squared: 0.5367
## F-statistic: 262.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.115394 -0.025866 -0.007482 0.020830 0.244891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.070e+00 2.651e-01 19.125 < 2e-16 ***
## time_value -2.632e-04 1.463e-05 -17.987 < 2e-16 ***
## EmergDec 5.854e+00 7.204e-01 8.126 2.13e-15 ***
## time_value:EmergDec -3.114e-04 3.914e-05 -7.956 7.52e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03872 on 673 degrees of freedom
## Multiple R-squared: 0.4748, Adjusted R-squared: 0.4724
## F-statistic: 202.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.246597 -0.027118 -0.008894 0.025734 0.153268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.091e+00 3.090e-01 16.47 <2e-16 ***
## time_value -2.652e-04 1.706e-05 -15.55 <2e-16 ***
## EmergDec 1.061e+01 8.124e-01 13.06 <2e-16 ***
## time_value:EmergDec -5.662e-04 4.415e-05 -12.82 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04453 on 673 degrees of freedom
## Multiple R-squared: 0.6181, Adjusted R-squared: 0.6164
## F-statistic: 363 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10652 -0.02145 -0.00412 0.01877 0.12753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.149e+00 2.183e-01 23.586 <2e-16 ***
## time_value -2.668e-04 1.205e-05 -22.138 <2e-16 ***
## EmergDec 6.082e-02 5.835e-01 0.104 0.917
## time_value:EmergDec 2.174e-06 3.171e-05 0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03167 on 673 degrees of freedom
## Multiple R-squared: 0.4667, Adjusted R-squared: 0.4643
## F-statistic: 196.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.162640 -0.020325 -0.003665 0.018208 0.113522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.436e+00 2.114e-01 25.720 < 2e-16 ***
## time_value -2.824e-04 1.167e-05 -24.204 < 2e-16 ***
## EmergDec 3.865e+00 5.697e-01 6.784 2.57e-11 ***
## time_value:EmergDec -2.028e-04 3.095e-05 -6.552 1.13e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03077 on 673 degrees of freedom
## Multiple R-squared: 0.5953, Adjusted R-squared: 0.5935
## F-statistic: 329.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.227829 -0.025142 -0.007099 0.024318 0.152839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.154e+00 2.945e-01 17.501 <2e-16 ***
## time_value -2.672e-04 1.626e-05 -16.431 <2e-16 ***
## EmergDec 7.682e+00 7.617e-01 10.085 <2e-16 ***
## time_value:EmergDec -4.086e-04 4.140e-05 -9.869 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04215 on 673 degrees of freedom
## Multiple R-squared: 0.5529, Adjusted R-squared: 0.5509
## F-statistic: 277.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.146683 -0.024999 -0.006934 0.021792 0.184164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.328e+00 2.530e-01 21.056 < 2e-16 ***
## time_value -2.773e-04 1.397e-05 -19.854 < 2e-16 ***
## EmergDec 5.014e+00 6.652e-01 7.537 1.55e-13 ***
## time_value:EmergDec -2.667e-04 3.615e-05 -7.378 4.76e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03646 on 673 degrees of freedom
## Multiple R-squared: 0.4953, Adjusted R-squared: 0.4931
## F-statistic: 220.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07365 -0.02343 -0.00721 0.01761 0.15659
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.486e+00 2.322e-01 19.317 < 2e-16 ***
## time_value -2.331e-04 1.282e-05 -18.187 < 2e-16 ***
## EmergDec 4.162e+00 6.419e-01 6.484 1.73e-10 ***
## time_value:EmergDec -2.215e-04 3.486e-05 -6.353 3.89e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03415 on 673 degrees of freedom
## Multiple R-squared: 0.4409, Adjusted R-squared: 0.4384
## F-statistic: 176.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14956 -0.02155 -0.00498 0.01945 0.12046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.092e+00 2.375e-01 21.441 < 2e-16 ***
## time_value -2.634e-04 1.311e-05 -20.090 < 2e-16 ***
## EmergDec 1.690e+00 6.192e-01 2.729 0.00652 **
## time_value:EmergDec -8.570e-05 3.365e-05 -2.547 0.01110 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0341 on 673 degrees of freedom
## Multiple R-squared: 0.4633, Adjusted R-squared: 0.4609
## F-statistic: 193.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.182181 -0.024204 -0.006774 0.021683 0.155600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.503e+00 2.781e-01 19.790 < 2e-16 ***
## time_value -2.863e-04 1.535e-05 -18.649 < 2e-16 ***
## EmergDec 4.867e+00 7.134e-01 6.822 2.00e-11 ***
## time_value:EmergDec -2.576e-04 3.877e-05 -6.643 6.34e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03966 on 673 degrees of freedom
## Multiple R-squared: 0.4761, Adjusted R-squared: 0.4738
## F-statistic: 203.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.180570 -0.023965 -0.006575 0.020807 0.173224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.719e+00 2.670e-01 21.420 < 2e-16 ***
## time_value -2.986e-04 1.474e-05 -20.259 < 2e-16 ***
## EmergDec 4.957e+00 7.019e-01 7.063 4.08e-12 ***
## time_value:EmergDec -2.618e-04 3.814e-05 -6.865 1.52e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03847 on 673 degrees of freedom
## Multiple R-squared: 0.5202, Adjusted R-squared: 0.518
## F-statistic: 243.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073057 -0.019195 -0.004634 0.015034 0.135415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.030e+00 2.042e-01 19.741 < 2e-16 ***
## time_value -2.076e-04 1.127e-05 -18.424 < 2e-16 ***
## EmergDec 2.582e+00 5.549e-01 4.652 3.95e-06 ***
## time_value:EmergDec -1.361e-04 3.014e-05 -4.514 7.50e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02982 on 673 degrees of freedom
## Multiple R-squared: 0.4216, Adjusted R-squared: 0.419
## F-statistic: 163.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080225 -0.027216 -0.006661 0.020125 0.201660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.085e+00 2.693e-01 18.881 < 2e-16 ***
## time_value -2.644e-04 1.487e-05 -17.785 < 2e-16 ***
## EmergDec 3.289e+00 7.319e-01 4.493 8.25e-06 ***
## time_value:EmergDec -1.746e-04 3.976e-05 -4.392 1.30e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03934 on 673 degrees of freedom
## Multiple R-squared: 0.4326, Adjusted R-squared: 0.4301
## F-statistic: 171.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11230 -0.02175 -0.00479 0.01886 0.16568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.337e+00 2.167e-01 20.009 < 2e-16 ***
## time_value -2.242e-04 1.196e-05 -18.738 < 2e-16 ***
## EmergDec 3.606e+00 5.842e-01 6.173 1.16e-09 ***
## time_value:EmergDec -1.913e-04 3.174e-05 -6.028 2.74e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03155 on 673 degrees of freedom
## Multiple R-squared: 0.4501, Adjusted R-squared: 0.4477
## F-statistic: 183.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09715 -0.02065 -0.00380 0.01673 0.11688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.549e+00 2.132e-01 21.340 <2e-16 ***
## time_value -2.358e-04 1.177e-05 -20.041 <2e-16 ***
## EmergDec 5.108e+00 5.794e-01 8.816 <2e-16 ***
## time_value:EmergDec -2.711e-04 3.148e-05 -8.613 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03114 on 673 degrees of freedom
## Multiple R-squared: 0.5441, Adjusted R-squared: 0.5421
## F-statistic: 267.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.151401 -0.023459 -0.008082 0.015514 0.157776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.154e+00 2.628e-01 19.610 < 2e-16 ***
## time_value -2.686e-04 1.451e-05 -18.508 < 2e-16 ***
## EmergDec 2.062e+00 6.743e-01 3.057 0.00232 **
## time_value:EmergDec -1.062e-04 3.665e-05 -2.898 0.00388 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03748 on 673 degrees of freedom
## Multiple R-squared: 0.4164, Adjusted R-squared: 0.4138
## F-statistic: 160 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.161008 -0.021667 -0.005693 0.018346 0.198565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.787e+00 2.275e-01 21.040 < 2e-16 ***
## time_value -2.481e-04 1.256e-05 -19.754 < 2e-16 ***
## EmergDec 4.465e+00 6.132e-01 7.281 9.31e-13 ***
## time_value:EmergDec -2.354e-04 3.332e-05 -7.064 4.04e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03312 on 673 degrees of freedom
## Multiple R-squared: 0.5533, Adjusted R-squared: 0.5513
## F-statistic: 277.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.127597 -0.025716 -0.002503 0.023175 0.194156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.766e+00 2.633e-01 25.696 <2e-16 ***
## time_value -3.550e-04 1.453e-05 -24.427 <2e-16 ***
## EmergDec 1.528e+00 7.157e-01 2.135 0.0332 *
## time_value:EmergDec -7.719e-05 3.888e-05 -1.985 0.0475 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03846 on 673 degrees of freedom
## Multiple R-squared: 0.5338, Adjusted R-squared: 0.5318
## F-statistic: 256.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.156309 -0.024259 -0.005259 0.021670 0.181113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.686e+00 2.894e-01 16.195 <2e-16 ***
## time_value -2.415e-04 1.598e-05 -15.114 <2e-16 ***
## EmergDec 1.346e+00 7.075e-01 1.902 0.0576 .
## time_value:EmergDec -6.643e-05 3.847e-05 -1.727 0.0847 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04042 on 673 degrees of freedom
## Multiple R-squared: 0.4004, Adjusted R-squared: 0.3977
## F-statistic: 149.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.166781 -0.026754 -0.007107 0.021540 0.158694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.790e+00 2.667e-01 21.708 < 2e-16 ***
## time_value -3.025e-04 1.472e-05 -20.547 < 2e-16 ***
## EmergDec 3.895e+00 7.188e-01 5.418 8.39e-08 ***
## time_value:EmergDec -2.054e-04 3.906e-05 -5.260 1.94e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03883 on 673 degrees of freedom
## Multiple R-squared: 0.4794, Adjusted R-squared: 0.4771
## F-statistic: 206.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079529 -0.024137 -0.005228 0.018867 0.136093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.384e+00 2.270e-01 28.129 <2e-16 ***
## time_value -3.353e-04 1.253e-05 -26.762 <2e-16 ***
## EmergDec 1.632e+00 6.327e-01 2.579 0.0101 *
## time_value:EmergDec -8.354e-05 3.436e-05 -2.431 0.0153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03349 on 673 degrees of freedom
## Multiple R-squared: 0.5888, Adjusted R-squared: 0.587
## F-statistic: 321.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071525 -0.026586 -0.007897 0.017685 0.161698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.035e+00 2.523e-01 19.957 < 2e-16 ***
## time_value -2.604e-04 1.393e-05 -18.699 < 2e-16 ***
## EmergDec 2.876e+00 6.857e-01 4.194 3.11e-05 ***
## time_value:EmergDec -1.524e-04 3.725e-05 -4.093 4.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03685 on 673 degrees of freedom
## Multiple R-squared: 0.458, Adjusted R-squared: 0.4556
## F-statistic: 189.6 on 3 and 673 DF, p-value: < 2.2e-16School Closure
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*SchoolClose,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.113825 -0.025961 -0.002819 0.026542 0.128292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.252e+00 1.530e-01 21.249 < 2e-16 ***
## time_value -1.617e-04 8.411e-06 -19.222 < 2e-16 ***
## SchoolClose 7.983e+00 1.588e+00 5.026 6.43e-07 ***
## time_value:SchoolClose -4.316e-04 8.632e-05 -5.000 7.33e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03847 on 673 degrees of freedom
## Multiple R-squared: 0.3846, Adjusted R-squared: 0.3818
## F-statistic: 140.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081719 -0.022119 -0.002799 0.019811 0.141825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.091e+00 1.297e-01 16.121 < 2e-16 ***
## time_value -1.015e-04 7.124e-06 -14.242 < 2e-16 ***
## SchoolClose 1.567e+01 2.146e+00 7.299 8.19e-13 ***
## time_value:SchoolClose -8.496e-04 1.167e-04 -7.279 9.42e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03413 on 673 degrees of freedom
## Multiple R-squared: 0.3187, Adjusted R-squared: 0.3157
## F-statistic: 104.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073620 -0.021604 -0.004841 0.017923 0.125186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.647e+00 1.429e-01 18.522 < 2e-16 ***
## time_value -1.316e-04 7.860e-06 -16.748 < 2e-16 ***
## SchoolClose 8.135e+00 1.064e+00 7.648 7.10e-14 ***
## time_value:SchoolClose -4.394e-04 5.777e-05 -7.606 9.54e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03375 on 673 degrees of freedom
## Multiple R-squared: 0.3682, Adjusted R-squared: 0.3654
## F-statistic: 130.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.091907 -0.023849 -0.003341 0.025266 0.107213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.285e+00 1.493e-01 15.309 < 2e-16 ***
## time_value -1.083e-04 8.210e-06 -13.189 < 2e-16 ***
## SchoolClose 4.651e+00 1.196e+00 3.890 0.000110 ***
## time_value:SchoolClose -2.492e-04 6.494e-05 -3.837 0.000136 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03589 on 673 degrees of freedom
## Multiple R-squared: 0.3342, Adjusted R-squared: 0.3312
## F-statistic: 112.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.110627 -0.019955 -0.004798 0.016761 0.115736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.004e+00 2.069e-01 24.19 <2e-16 ***
## time_value -2.598e-04 1.142e-05 -22.75 <2e-16 ***
## SchoolClose 5.938e+00 5.623e-01 10.56 <2e-16 ***
## time_value:SchoolClose -3.132e-04 3.055e-05 -10.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03022 on 673 degrees of freedom
## Multiple R-squared: 0.7219, Adjusted R-squared: 0.7206
## F-statistic: 582.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.102593 -0.024419 -0.002579 0.022147 0.179653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.404e+00 1.535e-01 15.66 <2e-16 ***
## time_value -1.150e-04 8.432e-06 -13.64 <2e-16 ***
## SchoolClose 2.078e+01 1.906e+00 10.91 <2e-16 ***
## time_value:SchoolClose -1.125e-03 1.036e-04 -10.87 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03916 on 673 degrees of freedom
## Multiple R-squared: 0.4241, Adjusted R-squared: 0.4215
## F-statistic: 165.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.111449 -0.027086 -0.003499 0.024553 0.242033
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.030e+00 1.641e-01 12.37 <2e-16 ***
## time_value -9.548e-05 9.021e-06 -10.58 <2e-16 ***
## SchoolClose 2.075e+01 1.724e+00 12.04 <2e-16 ***
## time_value:SchoolClose -1.122e-03 9.366e-05 -11.98 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04128 on 673 degrees of freedom
## Multiple R-squared: 0.5172, Adjusted R-squared: 0.5151
## F-statistic: 240.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081030 -0.021309 -0.004592 0.018198 0.169819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.619e+00 2.246e-01 11.66 <2e-16 ***
## time_value -1.269e-04 1.239e-05 -10.24 <2e-16 ***
## SchoolClose 9.128e+00 6.260e-01 14.58 <2e-16 ***
## time_value:SchoolClose -4.878e-04 3.400e-05 -14.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03314 on 673 degrees of freedom
## Multiple R-squared: 0.682, Adjusted R-squared: 0.6806
## F-statistic: 481.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.093040 -0.024825 -0.006167 0.016593 0.166592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.943e+00 2.424e-01 20.387 <2e-16 ***
## time_value -2.554e-04 1.338e-05 -19.086 <2e-16 ***
## SchoolClose 5.941e+00 6.590e-01 9.017 <2e-16 ***
## time_value:SchoolClose -3.158e-04 3.580e-05 -8.822 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03541 on 673 degrees of freedom
## Multiple R-squared: 0.5268, Adjusted R-squared: 0.5247
## F-statistic: 249.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.105270 -0.022168 -0.001969 0.021579 0.130148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.530e+00 1.463e-01 10.457 < 2e-16 ***
## time_value -6.815e-05 8.040e-06 -8.476 < 2e-16 ***
## SchoolClose 1.181e+01 1.441e+00 8.197 1.25e-15 ***
## time_value:SchoolClose -6.377e-04 7.828e-05 -8.146 1.83e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03644 on 673 degrees of freedom
## Multiple R-squared: 0.3877, Adjusted R-squared: 0.385
## F-statistic: 142.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.084242 -0.023890 -0.001934 0.020352 0.127183
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.746e+00 1.337e-01 13.057 <2e-16 ***
## time_value -8.121e-05 7.347e-06 -11.052 <2e-16 ***
## SchoolClose 1.572e+01 1.669e+00 9.418 <2e-16 ***
## time_value:SchoolClose -8.511e-04 9.073e-05 -9.380 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03432 on 673 degrees of freedom
## Multiple R-squared: 0.3797, Adjusted R-squared: 0.3769
## F-statistic: 137.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.106666 -0.024007 -0.003525 0.024183 0.153436
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.494e+00 1.793e-01 8.335 4.36e-16 ***
## time_value -6.729e-05 9.871e-06 -6.817 2.07e-11 ***
## SchoolClose 5.266e+00 9.981e-01 5.276 1.78e-07 ***
## time_value:SchoolClose -2.812e-04 5.418e-05 -5.190 2.78e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03901 on 673 degrees of freedom
## Multiple R-squared: 0.4097, Adjusted R-squared: 0.4071
## F-statistic: 155.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079593 -0.026832 -0.007375 0.019025 0.165356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.576e+00 2.612e-01 21.349 < 2e-16 ***
## time_value -2.924e-04 1.442e-05 -20.284 < 2e-16 ***
## SchoolClose 5.228e+00 7.281e-01 7.180 1.84e-12 ***
## time_value:SchoolClose -2.784e-04 3.955e-05 -7.039 4.79e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03854 on 673 degrees of freedom
## Multiple R-squared: 0.4947, Adjusted R-squared: 0.4924
## F-statistic: 219.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.084090 -0.025128 -0.003762 0.018820 0.125974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.999e+00 1.413e-01 21.224 < 2e-16 ***
## time_value -1.482e-04 7.762e-06 -19.088 < 2e-16 ***
## SchoolClose 1.277e+01 2.058e+00 6.206 9.48e-10 ***
## time_value:SchoolClose -6.916e-04 1.118e-04 -6.183 1.09e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03665 on 673 degrees of freedom
## Multiple R-squared: 0.3981, Adjusted R-squared: 0.3954
## F-statistic: 148.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.117641 -0.026846 -0.000537 0.023549 0.224757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.922e+00 1.476e-01 13.027 <2e-16 ***
## time_value -8.957e-05 8.105e-06 -11.052 <2e-16 ***
## SchoolClose 2.326e+01 2.405e+00 9.670 <2e-16 ***
## time_value:SchoolClose -1.259e-03 1.308e-04 -9.626 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03882 on 673 degrees of freedom
## Multiple R-squared: 0.5029, Adjusted R-squared: 0.5007
## F-statistic: 227 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08958 -0.02434 -0.00152 0.01722 0.16802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.863e+00 1.362e-01 21.02 <2e-16 ***
## time_value -1.417e-04 7.480e-06 -18.95 <2e-16 ***
## SchoolClose 2.190e+01 2.115e+00 10.36 <2e-16 ***
## time_value:SchoolClose -1.187e-03 1.150e-04 -10.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03563 on 673 degrees of freedom
## Multiple R-squared: 0.4869, Adjusted R-squared: 0.4846
## F-statistic: 212.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.098547 -0.026056 -0.001322 0.021672 0.178042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.747e+00 1.422e-01 19.317 <2e-16 ***
## time_value -1.356e-04 7.805e-06 -17.367 <2e-16 ***
## SchoolClose 9.638e+00 4.098e+00 2.352 0.019 *
## time_value:SchoolClose -5.200e-04 2.230e-04 -2.331 0.020 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03849 on 673 degrees of freedom
## Multiple R-squared: 0.4187, Adjusted R-squared: 0.4161
## F-statistic: 161.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060263 -0.024298 -0.004796 0.021060 0.158447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.738e+00 2.209e-01 25.982 < 2e-16 ***
## time_value -3.008e-04 1.219e-05 -24.671 < 2e-16 ***
## SchoolClose 2.696e+00 6.157e-01 4.380 1.38e-05 ***
## time_value:SchoolClose -1.412e-04 3.344e-05 -4.222 2.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03259 on 673 degrees of freedom
## Multiple R-squared: 0.5514, Adjusted R-squared: 0.5494
## F-statistic: 275.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.156787 -0.022669 -0.003016 0.019988 0.141535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.196e+00 1.274e-01 17.234 < 2e-16 ***
## time_value -1.062e-04 6.997e-06 -15.179 < 2e-16 ***
## SchoolClose 1.253e+01 1.988e+00 6.302 5.31e-10 ***
## time_value:SchoolClose -6.782e-04 1.081e-04 -6.274 6.32e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03351 on 673 degrees of freedom
## Multiple R-squared: 0.3675, Adjusted R-squared: 0.3647
## F-statistic: 130.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.108904 -0.026022 -0.002082 0.024543 0.218297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.696e+00 1.698e-01 9.987 < 2e-16 ***
## time_value -7.663e-05 9.341e-06 -8.203 1.19e-15 ***
## SchoolClose 1.999e+01 1.374e+00 14.548 < 2e-16 ***
## time_value:SchoolClose -1.080e-03 7.464e-05 -14.472 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04086 on 673 degrees of freedom
## Multiple R-squared: 0.5396, Adjusted R-squared: 0.5375
## F-statistic: 262.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.133514 -0.028199 -0.001454 0.026827 0.257196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.058e+00 1.722e-01 6.144 1.37e-09 ***
## time_value -4.105e-05 9.461e-06 -4.339 1.65e-05 ***
## SchoolClose 1.803e+01 1.907e+00 9.453 < 2e-16 ***
## time_value:SchoolClose -9.742e-04 1.036e-04 -9.400 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04365 on 673 degrees of freedom
## Multiple R-squared: 0.4593, Adjusted R-squared: 0.4569
## F-statistic: 190.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.097390 -0.024363 -0.001864 0.020203 0.242797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.489e+00 1.503e-01 23.203 < 2e-16 ***
## time_value -1.744e-04 8.262e-06 -21.112 < 2e-16 ***
## SchoolClose 1.478e+01 1.848e+00 7.995 5.65e-15 ***
## time_value:SchoolClose -7.999e-04 1.004e-04 -7.967 6.95e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03797 on 673 degrees of freedom
## Multiple R-squared: 0.4506, Adjusted R-squared: 0.4482
## F-statistic: 184 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.143952 -0.026483 -0.002328 0.025705 0.154182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.826e+00 1.512e-01 18.70 <2e-16 ***
## time_value -1.386e-04 8.304e-06 -16.69 <2e-16 ***
## SchoolClose 2.579e+01 2.282e+00 11.30 <2e-16 ***
## time_value:SchoolClose -1.397e-03 1.241e-04 -11.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03957 on 673 degrees of freedom
## Multiple R-squared: 0.5181, Adjusted R-squared: 0.516
## F-statistic: 241.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.096094 -0.026323 -0.002741 0.024078 0.153797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.394e+00 1.633e-01 14.66 <2e-16 ***
## time_value -1.153e-04 8.975e-06 -12.85 <2e-16 ***
## SchoolClose 1.691e+01 1.669e+00 10.13 <2e-16 ***
## time_value:SchoolClose -9.147e-04 9.067e-05 -10.09 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04085 on 673 degrees of freedom
## Multiple R-squared: 0.4148, Adjusted R-squared: 0.4122
## F-statistic: 159 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088162 -0.025406 -0.003396 0.019901 0.187614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.716e+00 1.448e-01 18.76 <2e-16 ***
## time_value -1.338e-04 7.952e-06 -16.83 <2e-16 ***
## SchoolClose 1.956e+01 1.958e+00 9.99 <2e-16 ***
## time_value:SchoolClose -1.060e-03 1.064e-04 -9.96 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03728 on 673 degrees of freedom
## Multiple R-squared: 0.4142, Adjusted R-squared: 0.4116
## F-statistic: 158.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07386 -0.02155 -0.00295 0.01654 0.14466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.997e+00 1.186e-01 16.838 < 2e-16 ***
## time_value -9.642e-05 6.509e-06 -14.812 < 2e-16 ***
## SchoolClose 2.073e+01 3.136e+00 6.611 7.77e-11 ***
## time_value:SchoolClose -1.125e-03 1.706e-04 -6.594 8.64e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03197 on 673 degrees of freedom
## Multiple R-squared: 0.3477, Adjusted R-squared: 0.3448
## F-statistic: 119.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.091803 -0.025740 -0.003505 0.020946 0.136383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.717e+00 1.399e-01 19.419 <2e-16 ***
## time_value -1.325e-04 7.678e-06 -17.253 <2e-16 ***
## SchoolClose -5.291e+00 6.143e+00 -0.861 0.389
## time_value:SchoolClose 2.912e-04 3.345e-04 0.870 0.384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0383 on 673 degrees of freedom
## Multiple R-squared: 0.339, Adjusted R-squared: 0.3361
## F-statistic: 115.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064816 -0.020474 -0.004477 0.016972 0.126229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.913e+00 2.077e-01 18.843 < 2e-16 ***
## time_value -2.004e-04 1.146e-05 -17.487 < 2e-16 ***
## SchoolClose 4.114e+00 5.789e-01 7.107 3.04e-12 ***
## time_value:SchoolClose -2.185e-04 3.144e-05 -6.951 8.60e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03064 on 673 degrees of freedom
## Multiple R-squared: 0.4436, Adjusted R-squared: 0.4411
## F-statistic: 178.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.107092 -0.025993 -0.005294 0.022949 0.160820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.220e+00 1.711e-01 18.812 < 2e-16 ***
## time_value -1.610e-04 9.403e-06 -17.118 < 2e-16 ***
## SchoolClose 1.181e+01 2.082e+00 5.672 2.10e-08 ***
## time_value:SchoolClose -6.395e-04 1.132e-04 -5.649 2.38e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04388 on 673 degrees of freedom
## Multiple R-squared: 0.3453, Adjusted R-squared: 0.3423
## F-statistic: 118.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.092182 -0.026045 -0.003285 0.022049 0.154089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.541e+00 1.569e-01 22.576 < 2e-16 ***
## time_value -1.792e-04 8.618e-06 -20.792 < 2e-16 ***
## SchoolClose 1.662e+01 2.198e+00 7.559 1.33e-13 ***
## time_value:SchoolClose -8.991e-04 1.194e-04 -7.529 1.65e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04021 on 673 degrees of freedom
## Multiple R-squared: 0.4534, Adjusted R-squared: 0.4509
## F-statistic: 186.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.120127 -0.024783 -0.002111 0.021487 0.268532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.385e+00 1.613e-01 14.79 < 2e-16 ***
## time_value -1.147e-04 8.859e-06 -12.94 < 2e-16 ***
## SchoolClose 1.527e+01 2.103e+00 7.26 1.07e-12 ***
## time_value:SchoolClose -8.257e-04 1.144e-04 -7.22 1.41e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04165 on 673 degrees of freedom
## Multiple R-squared: 0.3922, Adjusted R-squared: 0.3895
## F-statistic: 144.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.130505 -0.028340 -0.002308 0.025049 0.171646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.581e+00 1.779e-01 8.884 < 2e-16 ***
## time_value -7.106e-05 9.785e-06 -7.262 1.06e-12 ***
## SchoolClose 2.847e+01 1.527e+00 18.647 < 2e-16 ***
## time_value:SchoolClose -1.539e-03 8.294e-05 -18.561 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04324 on 673 degrees of freedom
## Multiple R-squared: 0.6398, Adjusted R-squared: 0.6382
## F-statistic: 398.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070889 -0.021668 -0.004627 0.017887 0.128296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.054e+00 2.117e-01 23.874 <2e-16 ***
## time_value -2.615e-04 1.169e-05 -22.382 <2e-16 ***
## SchoolClose 8.328e-01 5.902e-01 1.411 0.159
## time_value:SchoolClose -3.970e-05 3.205e-05 -1.238 0.216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03124 on 673 degrees of freedom
## Multiple R-squared: 0.4812, Adjusted R-squared: 0.4789
## F-statistic: 208.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.107529 -0.024389 0.000064 0.024540 0.117622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.831e+00 1.437e-01 12.738 < 2e-16 ***
## time_value -8.290e-05 7.891e-06 -10.506 < 2e-16 ***
## SchoolClose 1.389e+01 3.018e+00 4.604 4.97e-06 ***
## time_value:SchoolClose -7.513e-04 1.642e-04 -4.575 5.66e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03842 on 673 degrees of freedom
## Multiple R-squared: 0.369, Adjusted R-squared: 0.3662
## F-statistic: 131.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.136069 -0.025124 -0.001708 0.024883 0.181048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.974e+00 1.637e-01 12.06 <2e-16 ***
## time_value -9.122e-05 8.999e-06 -10.14 <2e-16 ***
## SchoolClose 2.292e+01 1.512e+00 15.16 <2e-16 ***
## time_value:SchoolClose -1.239e-03 8.215e-05 -15.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04031 on 673 degrees of freedom
## Multiple R-squared: 0.5911, Adjusted R-squared: 0.5892
## F-statistic: 324.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.103869 -0.024419 -0.001981 0.021686 0.205269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.917e+00 1.400e-01 20.84 <2e-16 ***
## time_value -1.440e-04 7.687e-06 -18.73 <2e-16 ***
## SchoolClose 2.252e+01 2.079e+00 10.83 <2e-16 ***
## time_value:SchoolClose -1.220e-03 1.131e-04 -10.79 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03655 on 673 degrees of freedom
## Multiple R-squared: 0.4929, Adjusted R-squared: 0.4906
## F-statistic: 218 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089668 -0.025750 -0.003575 0.018949 0.156860
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.680e+00 1.419e-01 18.884 < 2e-16 ***
## time_value -1.332e-04 7.800e-06 -17.080 < 2e-16 ***
## SchoolClose 1.210e+01 1.471e+00 8.227 9.95e-16 ***
## time_value:SchoolClose -6.548e-04 7.994e-05 -8.191 1.30e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03562 on 673 degrees of freedom
## Multiple R-squared: 0.3916, Adjusted R-squared: 0.3889
## F-statistic: 144.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10545 -0.02468 -0.00068 0.02259 0.14463
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.954e+00 1.447e-01 13.504 < 2e-16 ***
## time_value -8.978e-05 7.946e-06 -11.298 < 2e-16 ***
## SchoolClose 1.001e+01 2.151e+00 4.653 3.94e-06 ***
## time_value:SchoolClose -5.404e-04 1.170e-04 -4.619 4.61e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03781 on 673 degrees of freedom
## Multiple R-squared: 0.34, Adjusted R-squared: 0.3371
## F-statistic: 115.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14551 -0.02541 -0.00123 0.02371 0.18187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.520e+00 1.501e-01 16.782 <2e-16 ***
## time_value -1.213e-04 8.247e-06 -14.703 <2e-16 ***
## SchoolClose 2.130e+01 2.233e+00 9.538 <2e-16 ***
## time_value:SchoolClose -1.153e-03 1.214e-04 -9.495 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03924 on 673 degrees of freedom
## Multiple R-squared: 0.4869, Adjusted R-squared: 0.4846
## F-statistic: 212.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11701 -0.02537 -0.00374 0.02388 0.19779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.989e+00 1.803e-01 16.58 <2e-16 ***
## time_value -1.476e-04 9.927e-06 -14.87 <2e-16 ***
## SchoolClose 1.449e+01 1.040e+00 13.93 <2e-16 ***
## time_value:SchoolClose -7.817e-04 5.646e-05 -13.85 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03972 on 673 degrees of freedom
## Multiple R-squared: 0.4885, Adjusted R-squared: 0.4862
## F-statistic: 214.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077652 -0.021180 -0.001187 0.019570 0.125116
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.018e+00 1.231e-01 16.396 < 2e-16 ***
## time_value -9.629e-05 6.760e-06 -14.244 < 2e-16 ***
## SchoolClose 1.195e+01 1.976e+00 6.045 2.47e-09 ***
## time_value:SchoolClose -6.472e-04 1.075e-04 -6.022 2.83e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03237 on 673 degrees of freedom
## Multiple R-squared: 0.3187, Adjusted R-squared: 0.3156
## F-statistic: 104.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.097919 -0.027621 -0.003649 0.022156 0.209240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.060e+00 1.514e-01 20.220 <2e-16 ***
## time_value -1.523e-04 8.303e-06 -18.344 <2e-16 ***
## SchoolClose -1.664e+01 9.074e+00 -1.833 0.0672 .
## time_value:SchoolClose 9.097e-04 4.943e-04 1.841 0.0661 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04163 on 673 degrees of freedom
## Multiple R-squared: 0.3646, Adjusted R-squared: 0.3618
## F-statistic: 128.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089717 -0.023446 -0.001276 0.019976 0.152701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.426e+00 1.324e-01 18.328 <2e-16 ***
## time_value -1.185e-04 7.276e-06 -16.285 <2e-16 ***
## SchoolClose 1.153e+01 1.356e+00 8.504 <2e-16 ***
## time_value:SchoolClose -6.240e-04 7.371e-05 -8.466 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03321 on 673 degrees of freedom
## Multiple R-squared: 0.3908, Adjusted R-squared: 0.3881
## F-statistic: 143.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.097788 -0.024353 -0.002766 0.023800 0.121354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.889e+00 1.385e-01 13.637 <2e-16 ***
## time_value -8.862e-05 7.610e-06 -11.645 <2e-16 ***
## SchoolClose 1.514e+01 1.653e+00 9.158 <2e-16 ***
## time_value:SchoolClose -8.189e-04 8.985e-05 -9.114 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03525 on 673 degrees of freedom
## Multiple R-squared: 0.4157, Adjusted R-squared: 0.4131
## F-statistic: 159.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064975 -0.024729 -0.009033 0.015149 0.141794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.096e+00 2.363e-01 21.560 < 2e-16 ***
## time_value -2.653e-04 1.304e-05 -20.339 < 2e-16 ***
## SchoolClose 4.293e+00 6.588e-01 6.517 1.41e-10 ***
## time_value:SchoolClose -2.270e-04 3.578e-05 -6.343 4.14e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03487 on 673 degrees of freedom
## Multiple R-squared: 0.4948, Adjusted R-squared: 0.4926
## F-statistic: 219.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.111727 -0.025646 -0.001484 0.025057 0.228770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.486e+00 1.495e-01 9.939 < 2e-16 ***
## time_value -6.547e-05 8.213e-06 -7.972 6.71e-15 ***
## SchoolClose 1.431e+01 2.065e+00 6.930 9.89e-12 ***
## time_value:SchoolClose -7.735e-04 1.123e-04 -6.890 1.29e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03883 on 673 degrees of freedom
## Multiple R-squared: 0.3861, Adjusted R-squared: 0.3834
## F-statistic: 141.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.124791 -0.025083 -0.001614 0.027387 0.216716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.265e+00 1.847e-01 23.093 < 2e-16 ***
## time_value -2.167e-04 1.016e-05 -21.320 < 2e-16 ***
## SchoolClose 7.705e+00 1.163e+00 6.622 7.24e-11 ***
## time_value:SchoolClose -4.150e-04 6.317e-05 -6.571 1.00e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04166 on 673 degrees of freedom
## Multiple R-squared: 0.453, Adjusted R-squared: 0.4506
## F-statistic: 185.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10098 -0.02654 -0.00330 0.02421 0.20631
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.434e+00 1.640e-01 8.745 < 2e-16 ***
## time_value -6.161e-05 9.018e-06 -6.832 1.88e-11 ***
## SchoolClose 1.538e+01 1.556e+00 9.883 < 2e-16 ***
## time_value:SchoolClose -8.311e-04 8.454e-05 -9.832 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04062 on 673 degrees of freedom
## Multiple R-squared: 0.3942, Adjusted R-squared: 0.3915
## F-statistic: 146 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.106494 -0.027022 -0.002347 0.023470 0.182672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.887e+00 1.541e-01 18.729 < 2e-16 ***
## time_value -1.419e-04 8.464e-06 -16.766 < 2e-16 ***
## SchoolClose 2.112e+01 2.721e+00 7.762 3.11e-14 ***
## time_value:SchoolClose -1.144e-03 1.480e-04 -7.733 3.84e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04074 on 673 degrees of freedom
## Multiple R-squared: 0.4268, Adjusted R-squared: 0.4242
## F-statistic: 167 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081412 -0.024107 -0.004942 0.018954 0.135753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.433e+00 2.289e-01 28.105 <2e-16 ***
## time_value -3.379e-04 1.263e-05 -26.749 <2e-16 ***
## SchoolClose 1.363e+00 6.273e-01 2.172 0.0302 *
## time_value:SchoolClose -6.893e-05 3.408e-05 -2.023 0.0435 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03355 on 673 degrees of freedom
## Multiple R-squared: 0.5875, Adjusted R-squared: 0.5856
## F-statistic: 319.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081084 -0.026943 -0.003561 0.018717 0.161433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.313e+00 1.423e-01 23.279 < 2e-16 ***
## time_value -1.651e-04 7.814e-06 -21.134 < 2e-16 ***
## SchoolClose 1.255e+01 2.878e+00 4.361 1.50e-05 ***
## time_value:SchoolClose -6.800e-04 1.566e-04 -4.344 1.62e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0379 on 673 degrees of freedom
## Multiple R-squared: 0.4268, Adjusted R-squared: 0.4242
## F-statistic: 167 on 3 and 673 DF, p-value: < 2.2e-16Bar Restriction
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*BarRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.117085 -0.024855 -0.001525 0.025576 0.120719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.008e+00 1.422e-01 21.152 <2e-16 ***
## time_value -1.481e-04 7.804e-06 -18.982 <2e-16 ***
## BarRestrict 1.117e+01 4.585e+00 2.436 0.0151 *
## time_value:BarRestrict -6.049e-04 2.496e-04 -2.424 0.0156 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0386 on 673 degrees of freedom
## Multiple R-squared: 0.3801, Adjusted R-squared: 0.3774
## F-statistic: 137.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07190 -0.02256 -0.00567 0.01790 0.15351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.893e+00 2.169e-01 17.949 < 2e-16 ***
## time_value -2.011e-04 1.197e-05 -16.803 < 2e-16 ***
## BarRestrict 3.339e+00 6.204e-01 5.382 1.02e-07 ***
## time_value:BarRestrict -1.774e-04 3.369e-05 -5.265 1.89e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03233 on 673 degrees of freedom
## Multiple R-squared: 0.3889, Adjusted R-squared: 0.3861
## F-statistic: 142.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06290 -0.02266 -0.00673 0.01728 0.13065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.002e+00 2.189e-01 18.282 < 2e-16 ***
## time_value -2.066e-04 1.208e-05 -17.101 < 2e-16 ***
## BarRestrict 3.829e+00 6.260e-01 6.117 1.62e-09 ***
## time_value:BarRestrict -2.041e-04 3.399e-05 -6.003 3.17e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03262 on 673 degrees of freedom
## Multiple R-squared: 0.4096, Adjusted R-squared: 0.407
## F-statistic: 155.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072551 -0.022397 -0.003798 0.017718 0.139349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.022e+00 2.148e-01 18.719 < 2e-16 ***
## time_value -2.043e-04 1.185e-05 -17.232 < 2e-16 ***
## BarRestrict 4.550e+00 6.833e-01 6.659 5.73e-11 ***
## time_value:BarRestrict -2.417e-04 3.708e-05 -6.520 1.38e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03332 on 673 degrees of freedom
## Multiple R-squared: 0.426, Adjusted R-squared: 0.4235
## F-statistic: 166.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072793 -0.020070 -0.005262 0.016497 0.119157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.832e+00 2.057e-01 23.50 <2e-16 ***
## time_value -2.503e-04 1.135e-05 -22.05 <2e-16 ***
## BarRestrict 6.660e+00 5.733e-01 11.62 <2e-16 ***
## time_value:BarRestrict -3.525e-04 3.114e-05 -11.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03035 on 673 degrees of freedom
## Multiple R-squared: 0.7195, Adjusted R-squared: 0.7182
## F-statistic: 575.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073780 -0.025394 -0.008116 0.021152 0.168897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.790e+00 2.474e-01 19.360 <2e-16 ***
## time_value -2.470e-04 1.366e-05 -18.090 <2e-16 ***
## BarRestrict 6.945e+00 6.956e-01 9.985 <2e-16 ***
## time_value:BarRestrict -3.709e-04 3.778e-05 -9.819 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03663 on 673 degrees of freedom
## Multiple R-squared: 0.4962, Adjusted R-squared: 0.494
## F-statistic: 221 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.082806 -0.027542 -0.008767 0.019732 0.218045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.711e+00 2.636e-01 17.88 <2e-16 ***
## time_value -2.438e-04 1.455e-05 -16.76 <2e-16 ***
## BarRestrict 9.846e+00 7.348e-01 13.40 <2e-16 ***
## time_value:BarRestrict -5.267e-04 3.991e-05 -13.20 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03889 on 673 degrees of freedom
## Multiple R-squared: 0.5713, Adjusted R-squared: 0.5694
## F-statistic: 299 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081030 -0.021309 -0.004592 0.018198 0.169819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.619e+00 2.246e-01 11.66 <2e-16 ***
## time_value -1.269e-04 1.239e-05 -10.24 <2e-16 ***
## BarRestrict 9.128e+00 6.260e-01 14.58 <2e-16 ***
## time_value:BarRestrict -4.878e-04 3.400e-05 -14.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03314 on 673 degrees of freedom
## Multiple R-squared: 0.682, Adjusted R-squared: 0.6806
## F-statistic: 481.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080551 -0.025135 -0.006188 0.016862 0.167481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.823e+00 2.394e-01 20.147 <2e-16 ***
## time_value -2.488e-04 1.321e-05 -18.830 <2e-16 ***
## BarRestrict 6.536e+00 6.673e-01 9.795 <2e-16 ***
## time_value:BarRestrict -3.481e-04 3.625e-05 -9.605 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03532 on 673 degrees of freedom
## Multiple R-squared: 0.5292, Adjusted R-squared: 0.5271
## F-statistic: 252.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.099256 -0.022331 -0.003544 0.019611 0.124963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.175e+00 1.671e-01 13.02 <2e-16 ***
## time_value -1.039e-04 9.204e-06 -11.29 <2e-16 ***
## BarRestrict 9.079e+00 8.532e-01 10.64 <2e-16 ***
## time_value:BarRestrict -4.886e-04 4.631e-05 -10.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03513 on 673 degrees of freedom
## Multiple R-squared: 0.4309, Adjusted R-squared: 0.4284
## F-statistic: 169.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063657 -0.021225 -0.006285 0.016656 0.157396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.525e+00 2.196e-01 16.053 <2e-16 ***
## time_value -1.796e-04 1.212e-05 -14.820 <2e-16 ***
## BarRestrict 5.981e+00 6.560e-01 9.117 <2e-16 ***
## time_value:BarRestrict -3.198e-04 3.561e-05 -8.980 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03328 on 673 degrees of freedom
## Multiple R-squared: 0.4166, Adjusted R-squared: 0.414
## F-statistic: 160.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.098179 -0.020605 -0.002689 0.017212 0.150900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.633e+00 2.018e-01 22.956 < 2e-16 ***
## time_value -2.409e-04 1.114e-05 -21.625 < 2e-16 ***
## BarRestrict 1.976e+00 5.674e-01 3.482 0.00053 ***
## time_value:BarRestrict -9.926e-05 3.082e-05 -3.221 0.00134 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02988 on 673 degrees of freedom
## Multiple R-squared: 0.6537, Adjusted R-squared: 0.6522
## F-statistic: 423.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069766 -0.026906 -0.007326 0.019199 0.165327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.552e+00 2.597e-01 21.376 < 2e-16 ***
## time_value -2.911e-04 1.433e-05 -20.306 < 2e-16 ***
## BarRestrict 5.413e+00 7.302e-01 7.413 3.72e-13 ***
## time_value:BarRestrict -2.884e-04 3.966e-05 -7.272 9.87e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03845 on 673 degrees of freedom
## Multiple R-squared: 0.497, Adjusted R-squared: 0.4947
## F-statistic: 221.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064263 -0.024514 -0.007189 0.017043 0.120460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.956e+00 2.268e-01 21.858 < 2e-16 ***
## time_value -2.564e-04 1.251e-05 -20.493 < 2e-16 ***
## BarRestrict 3.148e+00 6.834e-01 4.607 4.89e-06 ***
## time_value:BarRestrict -1.666e-04 3.710e-05 -4.491 8.35e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03448 on 673 degrees of freedom
## Multiple R-squared: 0.4674, Adjusted R-squared: 0.465
## F-statistic: 196.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079078 -0.025880 -0.005612 0.020277 0.199295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.001e+00 2.386e-01 20.96 <2e-16 ***
## time_value -2.599e-04 1.317e-05 -19.73 <2e-16 ***
## BarRestrict 8.188e+00 6.652e-01 12.31 <2e-16 ***
## time_value:BarRestrict -4.370e-04 3.613e-05 -12.10 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03521 on 673 degrees of freedom
## Multiple R-squared: 0.591, Adjusted R-squared: 0.5892
## F-statistic: 324.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06679 -0.02535 -0.00653 0.02123 0.15068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.840e+00 2.359e-01 20.519 <2e-16 ***
## time_value -2.511e-04 1.302e-05 -19.285 <2e-16 ***
## BarRestrict 6.580e+00 6.576e-01 10.006 <2e-16 ***
## time_value:BarRestrict -3.521e-04 3.572e-05 -9.857 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03481 on 673 degrees of freedom
## Multiple R-squared: 0.5104, Adjusted R-squared: 0.5082
## F-statistic: 233.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.100310 -0.026156 -0.002284 0.020869 0.177787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.666e+00 1.437e-01 18.560 < 2e-16 ***
## time_value -1.310e-04 7.884e-06 -16.622 < 2e-16 ***
## BarRestrict 2.726e+01 5.629e+00 4.843 1.58e-06 ***
## time_value:BarRestrict -1.479e-03 3.063e-04 -4.828 1.71e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03904 on 673 degrees of freedom
## Multiple R-squared: 0.4021, Adjusted R-squared: 0.3994
## F-statistic: 150.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060263 -0.024298 -0.004796 0.021060 0.158447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.738e+00 2.209e-01 25.982 < 2e-16 ***
## time_value -3.008e-04 1.219e-05 -24.671 < 2e-16 ***
## BarRestrict 2.696e+00 6.157e-01 4.380 1.38e-05 ***
## time_value:BarRestrict -1.412e-04 3.344e-05 -4.222 2.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03259 on 673 degrees of freedom
## Multiple R-squared: 0.5514, Adjusted R-squared: 0.5494
## F-statistic: 275.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068490 -0.021029 -0.003876 0.017537 0.143264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.037e+00 2.044e-01 19.746 <2e-16 ***
## time_value -2.081e-04 1.128e-05 -18.441 <2e-16 ***
## BarRestrict 5.381e+00 5.748e-01 9.362 <2e-16 ***
## time_value:BarRestrict -2.876e-04 3.122e-05 -9.214 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03027 on 673 degrees of freedom
## Multiple R-squared: 0.4838, Adjusted R-squared: 0.4815
## F-statistic: 210.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079058 -0.026434 -0.007243 0.021665 0.191646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.646e+00 2.501e-01 18.57 <2e-16 ***
## time_value -2.398e-04 1.381e-05 -17.37 <2e-16 ***
## BarRestrict 9.457e+00 7.032e-01 13.45 <2e-16 ***
## time_value:BarRestrict -5.049e-04 3.819e-05 -13.22 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03703 on 673 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.62
## F-statistic: 368.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095817 -0.026128 -0.007208 0.020799 0.225112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.514e+00 2.527e-01 17.86 <2e-16 ***
## time_value -2.322e-04 1.395e-05 -16.65 <2e-16 ***
## BarRestrict 7.653e+00 7.045e-01 10.86 <2e-16 ***
## time_value:BarRestrict -4.070e-04 3.827e-05 -10.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03729 on 673 degrees of freedom
## Multiple R-squared: 0.6054, Adjusted R-squared: 0.6036
## F-statistic: 344.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073189 -0.023283 -0.005715 0.017847 0.225571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.480e+00 2.418e-01 22.658 < 2e-16 ***
## time_value -2.846e-04 1.335e-05 -21.322 < 2e-16 ***
## BarRestrict 3.888e+00 6.858e-01 5.669 2.13e-08 ***
## time_value:BarRestrict -2.064e-04 3.724e-05 -5.543 4.28e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03593 on 673 degrees of freedom
## Multiple R-squared: 0.508, Adjusted R-squared: 0.5058
## F-statistic: 231.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.114466 -0.027744 -0.007282 0.025274 0.148959
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.496e+00 1.972e-01 17.73 <2e-16 ***
## time_value -1.756e-04 1.086e-05 -16.17 <2e-16 ***
## BarRestrict 1.457e+01 9.167e-01 15.89 <2e-16 ***
## time_value:BarRestrict -7.863e-04 4.976e-05 -15.80 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03994 on 673 degrees of freedom
## Multiple R-squared: 0.5089, Adjusted R-squared: 0.5068
## F-statistic: 232.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073166 -0.027604 -0.007667 0.020993 0.167912
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.837e+00 2.604e-01 18.573 <2e-16 ***
## time_value -2.504e-04 1.437e-05 -17.421 <2e-16 ***
## BarRestrict 6.720e+00 7.322e-01 9.178 <2e-16 ***
## time_value:BarRestrict -3.584e-04 3.977e-05 -9.012 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03856 on 673 degrees of freedom
## Multiple R-squared: 0.4787, Adjusted R-squared: 0.4764
## F-statistic: 206 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065832 -0.025520 -0.007768 0.017189 0.169721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.610e+00 2.408e-01 19.143 < 2e-16 ***
## time_value -2.386e-04 1.329e-05 -17.954 < 2e-16 ***
## BarRestrict 5.397e+00 7.131e-01 7.568 1.25e-13 ***
## time_value:BarRestrict -2.882e-04 3.872e-05 -7.444 2.99e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03638 on 673 degrees of freedom
## Multiple R-squared: 0.4423, Adjusted R-squared: 0.4398
## F-statistic: 177.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061899 -0.022202 -0.005824 0.017238 0.161102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.278e+00 2.117e-01 15.487 < 2e-16 ***
## time_value -1.673e-04 1.168e-05 -14.322 < 2e-16 ***
## BarRestrict 4.011e+00 6.324e-01 6.343 4.13e-10 ***
## time_value:BarRestrict -2.146e-04 3.433e-05 -6.251 7.24e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03208 on 673 degrees of freedom
## Multiple R-squared: 0.3431, Adjusted R-squared: 0.3402
## F-statistic: 117.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067898 -0.026344 -0.005347 0.017755 0.121093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.744e+00 2.423e-01 19.577 < 2e-16 ***
## time_value -2.446e-04 1.337e-05 -18.290 < 2e-16 ***
## BarRestrict 1.989e+00 6.991e-01 2.845 0.00458 **
## time_value:BarRestrict -1.042e-04 3.796e-05 -2.745 0.00622 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03624 on 673 degrees of freedom
## Multiple R-squared: 0.4084, Adjusted R-squared: 0.4057
## F-statistic: 154.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065145 -0.020460 -0.004463 0.016808 0.126407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.889e+00 2.066e-01 18.824 < 2e-16 ***
## time_value -1.991e-04 1.140e-05 -17.461 < 2e-16 ***
## BarRestrict 4.265e+00 5.809e-01 7.342 6.09e-13 ***
## time_value:BarRestrict -2.267e-04 3.155e-05 -7.187 1.76e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03059 on 673 degrees of freedom
## Multiple R-squared: 0.4455, Adjusted R-squared: 0.443
## F-statistic: 180.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109620 -0.025571 -0.004662 0.023084 0.161807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.063e+00 1.591e-01 19.245 < 2e-16 ***
## time_value -1.523e-04 8.734e-06 -17.434 < 2e-16 ***
## BarRestrict 1.532e+01 5.377e+00 2.849 0.00452 **
## time_value:BarRestrict -8.299e-04 2.926e-04 -2.836 0.00471 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04323 on 673 degrees of freedom
## Multiple R-squared: 0.3646, Adjusted R-squared: 0.3617
## F-statistic: 128.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089927 -0.027034 -0.005149 0.019949 0.161567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.737e+00 1.811e-01 20.63 <2e-16 ***
## time_value -1.900e-04 9.968e-06 -19.06 <2e-16 ***
## BarRestrict 1.257e+01 1.237e+00 10.17 <2e-16 ***
## time_value:BarRestrict -6.793e-04 6.709e-05 -10.13 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04059 on 673 degrees of freedom
## Multiple R-squared: 0.4429, Adjusted R-squared: 0.4404
## F-statistic: 178.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.117117 -0.024278 -0.003638 0.021443 0.266197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.688e+00 1.721e-01 15.62 <2e-16 ***
## time_value -1.315e-04 9.468e-06 -13.88 <2e-16 ***
## BarRestrict 1.368e+01 1.315e+00 10.41 <2e-16 ***
## time_value:BarRestrict -7.388e-04 7.140e-05 -10.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04096 on 673 degrees of freedom
## Multiple R-squared: 0.4122, Adjusted R-squared: 0.4096
## F-statistic: 157.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08900 -0.02680 -0.01030 0.02484 0.15150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.921e+00 2.703e-01 18.20 <2e-16 ***
## time_value -2.558e-04 1.492e-05 -17.15 <2e-16 ***
## BarRestrict 1.348e+01 7.536e-01 17.89 <2e-16 ***
## time_value:BarRestrict -7.214e-04 4.093e-05 -17.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03989 on 673 degrees of freedom
## Multiple R-squared: 0.6935, Adjusted R-squared: 0.6921
## F-statistic: 507.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072158 -0.022042 -0.004917 0.018312 0.129049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.962e+00 2.099e-01 23.641 <2e-16 ***
## time_value -2.564e-04 1.158e-05 -22.136 <2e-16 ***
## BarRestrict 1.226e+00 6.003e-01 2.043 0.0415 *
## time_value:BarRestrict -6.109e-05 3.260e-05 -1.874 0.0613 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03128 on 673 degrees of freedom
## Multiple R-squared: 0.4797, Adjusted R-squared: 0.4774
## F-statistic: 206.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067508 -0.020781 -0.004949 0.015887 0.172162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.025e+00 2.084e-01 24.111 <2e-16 ***
## time_value -2.596e-04 1.150e-05 -22.571 <2e-16 ***
## BarRestrict 5.476e+00 6.064e-01 9.030 <2e-16 ***
## time_value:BarRestrict -2.905e-04 3.293e-05 -8.822 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03127 on 673 degrees of freedom
## Multiple R-squared: 0.5819, Adjusted R-squared: 0.5801
## F-statistic: 312.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.107803 -0.026297 -0.008376 0.023005 0.153619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.044e+00 2.476e-01 20.37 <2e-16 ***
## time_value -2.610e-04 1.366e-05 -19.10 <2e-16 ***
## BarRestrict 1.096e+01 6.901e-01 15.88 <2e-16 ***
## time_value:BarRestrict -5.861e-04 3.749e-05 -15.63 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03653 on 673 degrees of freedom
## Multiple R-squared: 0.6641, Adjusted R-squared: 0.6626
## F-statistic: 443.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.090854 -0.024991 -0.007051 0.021346 0.184549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.273e+00 2.371e-01 22.245 <2e-16 ***
## time_value -2.743e-04 1.308e-05 -20.966 <2e-16 ***
## BarRestrict 6.392e+00 6.552e-01 9.756 <2e-16 ***
## time_value:BarRestrict -3.413e-04 3.559e-05 -9.589 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03486 on 673 degrees of freedom
## Multiple R-squared: 0.5386, Adjusted R-squared: 0.5365
## F-statistic: 261.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095004 -0.025269 -0.004117 0.017279 0.157717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.346e+00 1.346e-01 17.437 < 2e-16 ***
## time_value -1.147e-04 7.385e-06 -15.531 < 2e-16 ***
## BarRestrict 3.492e+01 4.754e+00 7.345 5.98e-13 ***
## time_value:BarRestrict -1.896e-03 2.586e-04 -7.331 6.55e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03643 on 673 degrees of freedom
## Multiple R-squared: 0.3638, Adjusted R-squared: 0.361
## F-statistic: 128.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079254 -0.022240 -0.005864 0.018478 0.120983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.005e+00 2.143e-01 23.353 < 2e-16 ***
## time_value -2.586e-04 1.183e-05 -21.859 < 2e-16 ***
## BarRestrict 3.693e+00 6.025e-01 6.130 1.50e-09 ***
## time_value:BarRestrict -1.942e-04 3.272e-05 -5.933 4.76e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03173 on 673 degrees of freedom
## Multiple R-squared: 0.5353, Adjusted R-squared: 0.5332
## F-statistic: 258.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077789 -0.024703 -0.007806 0.021095 0.156303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.404e+00 2.389e-01 22.62 <2e-16 ***
## time_value -2.808e-04 1.318e-05 -21.30 <2e-16 ***
## BarRestrict 7.959e+00 6.716e-01 11.85 <2e-16 ***
## time_value:BarRestrict -4.249e-04 3.648e-05 -11.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03537 on 673 degrees of freedom
## Multiple R-squared: 0.5833, Adjusted R-squared: 0.5814
## F-statistic: 314 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083890 -0.024966 -0.007786 0.020549 0.174227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.577e+00 2.415e-01 23.09 <2e-16 ***
## time_value -2.907e-04 1.333e-05 -21.81 <2e-16 ***
## BarRestrict 7.187e+00 6.791e-01 10.58 <2e-16 ***
## time_value:BarRestrict -3.826e-04 3.688e-05 -10.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03576 on 673 degrees of freedom
## Multiple R-squared: 0.5853, Adjusted R-squared: 0.5835
## F-statistic: 316.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061029 -0.019118 -0.004197 0.015461 0.131638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.935e+00 1.993e-01 19.749 < 2e-16 ***
## time_value -2.024e-04 1.100e-05 -18.400 < 2e-16 ***
## BarRestrict 3.211e+00 5.651e-01 5.683 1.97e-08 ***
## time_value:BarRestrict -1.703e-04 3.069e-05 -5.548 4.15e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0296 on 673 degrees of freedom
## Multiple R-squared: 0.43, Adjusted R-squared: 0.4275
## F-statistic: 169.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.100199 -0.027587 -0.004691 0.021257 0.208758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.963e+00 1.539e-01 19.253 <2e-16 ***
## time_value -1.469e-04 8.441e-06 -17.400 <2e-16 ***
## BarRestrict 5.310e+01 2.458e+01 2.161 0.0311 *
## time_value:BarRestrict -2.887e-03 1.338e-03 -2.158 0.0313 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04256 on 673 degrees of freedom
## Multiple R-squared: 0.3357, Adjusted R-squared: 0.3327
## F-statistic: 113.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075136 -0.023935 -0.003648 0.019423 0.154200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.805e+00 1.614e-01 17.38 <2e-16 ***
## time_value -1.394e-04 8.892e-06 -15.68 <2e-16 ***
## BarRestrict 8.186e+00 8.065e-01 10.15 <2e-16 ***
## time_value:BarRestrict -4.414e-04 4.376e-05 -10.09 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03317 on 673 degrees of freedom
## Multiple R-squared: 0.3924, Adjusted R-squared: 0.3897
## F-statistic: 144.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071385 -0.021271 -0.004936 0.016119 0.174623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.182e+00 2.123e-01 19.70 <2e-16 ***
## time_value -2.155e-04 1.172e-05 -18.39 <2e-16 ***
## BarRestrict 6.359e+00 6.178e-01 10.29 <2e-16 ***
## time_value:BarRestrict -3.392e-04 3.355e-05 -10.11 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03186 on 673 degrees of freedom
## Multiple R-squared: 0.5228, Adjusted R-squared: 0.5207
## F-statistic: 245.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064669 -0.025118 -0.009098 0.014791 0.139568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.960e+00 2.358e-01 21.032 < 2e-16 ***
## time_value -2.578e-04 1.301e-05 -19.808 < 2e-16 ***
## BarRestrict 4.727e+00 6.744e-01 7.009 5.85e-12 ***
## time_value:BarRestrict -2.506e-04 3.663e-05 -6.842 1.75e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03515 on 673 degrees of freedom
## Multiple R-squared: 0.4868, Adjusted R-squared: 0.4846
## F-statistic: 212.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07316 -0.02407 -0.00613 0.01715 0.20372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.102e+00 2.293e-01 17.892 <2e-16 ***
## time_value -2.102e-04 1.265e-05 -16.611 <2e-16 ***
## BarRestrict 6.587e+00 6.910e-01 9.533 <2e-16 ***
## time_value:BarRestrict -3.511e-04 3.751e-05 -9.359 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03486 on 673 degrees of freedom
## Multiple R-squared: 0.5051, Adjusted R-squared: 0.5029
## F-statistic: 228.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095812 -0.025844 -0.003187 0.022885 0.194401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.731e+00 2.547e-01 26.433 < 2e-16 ***
## time_value -3.531e-04 1.406e-05 -25.123 < 2e-16 ***
## BarRestrict 2.351e+00 7.160e-01 3.284 0.00108 **
## time_value:BarRestrict -1.218e-04 3.889e-05 -3.132 0.00181 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0377 on 673 degrees of freedom
## Multiple R-squared: 0.552, Adjusted R-squared: 0.55
## F-statistic: 276.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074054 -0.024533 -0.005863 0.019432 0.182140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.512e+00 2.424e-01 18.61 < 2e-16 ***
## time_value -2.318e-04 1.338e-05 -17.33 < 2e-16 ***
## BarRestrict 5.223e+00 6.757e-01 7.73 3.92e-14 ***
## time_value:BarRestrict -2.763e-04 3.670e-05 -7.53 1.64e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03577 on 673 degrees of freedom
## Multiple R-squared: 0.5304, Adjusted R-squared: 0.5283
## F-statistic: 253.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109928 -0.027936 -0.001762 0.025551 0.183727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.666e+00 1.534e-01 17.378 <2e-16 ***
## time_value -1.296e-04 8.418e-06 -15.401 <2e-16 ***
## BarRestrict 4.488e+00 6.035e+00 0.744 0.457
## time_value:BarRestrict -2.389e-04 3.286e-04 -0.727 0.467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04188 on 673 degrees of freedom
## Multiple R-squared: 0.3944, Adjusted R-squared: 0.3917
## F-statistic: 146.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079456 -0.024201 -0.005322 0.019249 0.136427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.337e+00 2.250e-01 28.167 < 2e-16 ***
## time_value -3.326e-04 1.242e-05 -26.789 < 2e-16 ***
## BarRestrict 1.911e+00 6.380e-01 2.995 0.00285 **
## time_value:BarRestrict -9.869e-05 3.465e-05 -2.848 0.00453 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03342 on 673 degrees of freedom
## Multiple R-squared: 0.5905, Adjusted R-squared: 0.5887
## F-statistic: 323.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067598 -0.027399 -0.007896 0.018540 0.163806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.929e+00 2.460e-01 20.038 < 2e-16 ***
## time_value -2.546e-04 1.358e-05 -18.750 < 2e-16 ***
## BarRestrict 3.580e+00 7.036e-01 5.088 4.71e-07 ***
## time_value:BarRestrict -1.906e-04 3.821e-05 -4.990 7.70e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03667 on 673 degrees of freedom
## Multiple R-squared: 0.4634, Adjusted R-squared: 0.461
## F-statistic: 193.7 on 3 and 673 DF, p-value: < 2.2e-16Restaurant Restriction
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
lm.fit <- lm(value~time_value*RestaurantRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.117085 -0.024855 -0.001525 0.025576 0.120719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.008e+00 1.422e-01 21.152 <2e-16 ***
## time_value -1.481e-04 7.804e-06 -18.982 <2e-16 ***
## RestaurantRestrict 1.117e+01 4.585e+00 2.436 0.0151 *
## time_value:RestaurantRestrict -6.049e-04 2.496e-04 -2.424 0.0156 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0386 on 673 degrees of freedom
## Multiple R-squared: 0.3801, Adjusted R-squared: 0.3774
## F-statistic: 137.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07190 -0.02256 -0.00567 0.01790 0.15351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.893e+00 2.169e-01 17.949 < 2e-16 ***
## time_value -2.011e-04 1.197e-05 -16.803 < 2e-16 ***
## RestaurantRestrict 3.339e+00 6.204e-01 5.382 1.02e-07 ***
## time_value:RestaurantRestrict -1.774e-04 3.369e-05 -5.265 1.89e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03233 on 673 degrees of freedom
## Multiple R-squared: 0.3889, Adjusted R-squared: 0.3861
## F-statistic: 142.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06290 -0.02266 -0.00673 0.01728 0.13065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.002e+00 2.189e-01 18.282 < 2e-16 ***
## time_value -2.066e-04 1.208e-05 -17.101 < 2e-16 ***
## RestaurantRestrict 3.829e+00 6.260e-01 6.117 1.62e-09 ***
## time_value:RestaurantRestrict -2.041e-04 3.399e-05 -6.003 3.17e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03262 on 673 degrees of freedom
## Multiple R-squared: 0.4096, Adjusted R-squared: 0.407
## F-statistic: 155.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072551 -0.022397 -0.003798 0.017718 0.139349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.022e+00 2.148e-01 18.719 < 2e-16 ***
## time_value -2.043e-04 1.185e-05 -17.232 < 2e-16 ***
## RestaurantRestrict 4.550e+00 6.833e-01 6.659 5.73e-11 ***
## time_value:RestaurantRestrict -2.417e-04 3.708e-05 -6.520 1.38e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03332 on 673 degrees of freedom
## Multiple R-squared: 0.426, Adjusted R-squared: 0.4235
## F-statistic: 166.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072793 -0.020070 -0.005262 0.016497 0.119157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.832e+00 2.057e-01 23.50 <2e-16 ***
## time_value -2.503e-04 1.135e-05 -22.05 <2e-16 ***
## RestaurantRestrict 6.660e+00 5.733e-01 11.62 <2e-16 ***
## time_value:RestaurantRestrict -3.525e-04 3.114e-05 -11.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03035 on 673 degrees of freedom
## Multiple R-squared: 0.7195, Adjusted R-squared: 0.7182
## F-statistic: 575.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073780 -0.025394 -0.008116 0.021152 0.168897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.790e+00 2.474e-01 19.360 <2e-16 ***
## time_value -2.470e-04 1.366e-05 -18.090 <2e-16 ***
## RestaurantRestrict 6.945e+00 6.956e-01 9.985 <2e-16 ***
## time_value:RestaurantRestrict -3.709e-04 3.778e-05 -9.819 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03663 on 673 degrees of freedom
## Multiple R-squared: 0.4962, Adjusted R-squared: 0.494
## F-statistic: 221 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.082806 -0.027542 -0.008767 0.019732 0.218045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.711e+00 2.636e-01 17.88 <2e-16 ***
## time_value -2.438e-04 1.455e-05 -16.76 <2e-16 ***
## RestaurantRestrict 9.846e+00 7.348e-01 13.40 <2e-16 ***
## time_value:RestaurantRestrict -5.267e-04 3.991e-05 -13.20 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03889 on 673 degrees of freedom
## Multiple R-squared: 0.5713, Adjusted R-squared: 0.5694
## F-statistic: 299 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081030 -0.021309 -0.004592 0.018198 0.169819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.619e+00 2.246e-01 11.66 <2e-16 ***
## time_value -1.269e-04 1.239e-05 -10.24 <2e-16 ***
## RestaurantRestrict 9.128e+00 6.260e-01 14.58 <2e-16 ***
## time_value:RestaurantRestrict -4.878e-04 3.400e-05 -14.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03314 on 673 degrees of freedom
## Multiple R-squared: 0.682, Adjusted R-squared: 0.6806
## F-statistic: 481.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080551 -0.025135 -0.006188 0.016862 0.167481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.823e+00 2.394e-01 20.147 <2e-16 ***
## time_value -2.488e-04 1.321e-05 -18.830 <2e-16 ***
## RestaurantRestrict 6.536e+00 6.673e-01 9.795 <2e-16 ***
## time_value:RestaurantRestrict -3.481e-04 3.625e-05 -9.605 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03532 on 673 degrees of freedom
## Multiple R-squared: 0.5292, Adjusted R-squared: 0.5271
## F-statistic: 252.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.099256 -0.022331 -0.003544 0.019611 0.124963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.175e+00 1.671e-01 13.02 <2e-16 ***
## time_value -1.039e-04 9.204e-06 -11.29 <2e-16 ***
## RestaurantRestrict 9.079e+00 8.532e-01 10.64 <2e-16 ***
## time_value:RestaurantRestrict -4.886e-04 4.631e-05 -10.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03513 on 673 degrees of freedom
## Multiple R-squared: 0.4309, Adjusted R-squared: 0.4284
## F-statistic: 169.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071770 -0.023221 -0.007309 0.016708 0.147587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.925e+00 2.302e-01 12.708 <2e-16 ***
## time_value -1.463e-04 1.270e-05 -11.525 <2e-16 ***
## RestaurantRestrict 6.429e+00 7.524e-01 8.545 <2e-16 ***
## time_value:RestaurantRestrict -3.448e-04 4.082e-05 -8.448 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03605 on 673 degrees of freedom
## Multiple R-squared: 0.3156, Adjusted R-squared: 0.3125
## F-statistic: 103.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.098179 -0.020605 -0.002689 0.017212 0.150900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.633e+00 2.018e-01 22.956 < 2e-16 ***
## time_value -2.409e-04 1.114e-05 -21.625 < 2e-16 ***
## RestaurantRestrict 1.976e+00 5.674e-01 3.482 0.00053 ***
## time_value:RestaurantRestrict -9.926e-05 3.082e-05 -3.221 0.00134 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02988 on 673 degrees of freedom
## Multiple R-squared: 0.6537, Adjusted R-squared: 0.6522
## F-statistic: 423.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069766 -0.026906 -0.007326 0.019199 0.165327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.552e+00 2.597e-01 21.376 < 2e-16 ***
## time_value -2.911e-04 1.433e-05 -20.306 < 2e-16 ***
## RestaurantRestrict 5.413e+00 7.302e-01 7.413 3.72e-13 ***
## time_value:RestaurantRestrict -2.884e-04 3.966e-05 -7.272 9.87e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03845 on 673 degrees of freedom
## Multiple R-squared: 0.497, Adjusted R-squared: 0.4947
## F-statistic: 221.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064263 -0.024514 -0.007189 0.017043 0.120460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.956e+00 2.268e-01 21.858 < 2e-16 ***
## time_value -2.564e-04 1.251e-05 -20.493 < 2e-16 ***
## RestaurantRestrict 3.148e+00 6.834e-01 4.607 4.89e-06 ***
## time_value:RestaurantRestrict -1.666e-04 3.710e-05 -4.491 8.35e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03448 on 673 degrees of freedom
## Multiple R-squared: 0.4674, Adjusted R-squared: 0.465
## F-statistic: 196.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079078 -0.025880 -0.005612 0.020277 0.199295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.001e+00 2.386e-01 20.96 <2e-16 ***
## time_value -2.599e-04 1.317e-05 -19.73 <2e-16 ***
## RestaurantRestrict 8.188e+00 6.652e-01 12.31 <2e-16 ***
## time_value:RestaurantRestrict -4.370e-04 3.613e-05 -12.10 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03521 on 673 degrees of freedom
## Multiple R-squared: 0.591, Adjusted R-squared: 0.5892
## F-statistic: 324.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06679 -0.02535 -0.00653 0.02123 0.15068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.840e+00 2.359e-01 20.519 <2e-16 ***
## time_value -2.511e-04 1.302e-05 -19.285 <2e-16 ***
## RestaurantRestrict 6.580e+00 6.576e-01 10.006 <2e-16 ***
## time_value:RestaurantRestrict -3.521e-04 3.572e-05 -9.857 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03481 on 673 degrees of freedom
## Multiple R-squared: 0.5104, Adjusted R-squared: 0.5082
## F-statistic: 233.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.100310 -0.026156 -0.002284 0.020869 0.177787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.666e+00 1.437e-01 18.560 < 2e-16 ***
## time_value -1.310e-04 7.884e-06 -16.622 < 2e-16 ***
## RestaurantRestrict 2.726e+01 5.629e+00 4.843 1.58e-06 ***
## time_value:RestaurantRestrict -1.479e-03 3.063e-04 -4.828 1.71e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03904 on 673 degrees of freedom
## Multiple R-squared: 0.4021, Adjusted R-squared: 0.3994
## F-statistic: 150.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060263 -0.024298 -0.004796 0.021060 0.158447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.738e+00 2.209e-01 25.982 < 2e-16 ***
## time_value -3.008e-04 1.219e-05 -24.671 < 2e-16 ***
## RestaurantRestrict 2.696e+00 6.157e-01 4.380 1.38e-05 ***
## time_value:RestaurantRestrict -1.412e-04 3.344e-05 -4.222 2.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03259 on 673 degrees of freedom
## Multiple R-squared: 0.5514, Adjusted R-squared: 0.5494
## F-statistic: 275.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068490 -0.021029 -0.003876 0.017537 0.143264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.037e+00 2.044e-01 19.746 <2e-16 ***
## time_value -2.081e-04 1.128e-05 -18.441 <2e-16 ***
## RestaurantRestrict 5.381e+00 5.748e-01 9.362 <2e-16 ***
## time_value:RestaurantRestrict -2.876e-04 3.122e-05 -9.214 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03027 on 673 degrees of freedom
## Multiple R-squared: 0.4838, Adjusted R-squared: 0.4815
## F-statistic: 210.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079058 -0.026434 -0.007243 0.021665 0.191646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.646e+00 2.501e-01 18.57 <2e-16 ***
## time_value -2.398e-04 1.381e-05 -17.37 <2e-16 ***
## RestaurantRestrict 9.457e+00 7.032e-01 13.45 <2e-16 ***
## time_value:RestaurantRestrict -5.049e-04 3.819e-05 -13.22 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03703 on 673 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.62
## F-statistic: 368.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095817 -0.026128 -0.007208 0.020799 0.225112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.514e+00 2.527e-01 17.86 <2e-16 ***
## time_value -2.322e-04 1.395e-05 -16.65 <2e-16 ***
## RestaurantRestrict 7.653e+00 7.045e-01 10.86 <2e-16 ***
## time_value:RestaurantRestrict -4.070e-04 3.827e-05 -10.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03729 on 673 degrees of freedom
## Multiple R-squared: 0.6054, Adjusted R-squared: 0.6036
## F-statistic: 344.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073189 -0.023283 -0.005715 0.017847 0.225571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.480e+00 2.418e-01 22.658 < 2e-16 ***
## time_value -2.846e-04 1.335e-05 -21.322 < 2e-16 ***
## RestaurantRestrict 3.888e+00 6.858e-01 5.669 2.13e-08 ***
## time_value:RestaurantRestrict -2.064e-04 3.724e-05 -5.543 4.28e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03593 on 673 degrees of freedom
## Multiple R-squared: 0.508, Adjusted R-squared: 0.5058
## F-statistic: 231.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.114466 -0.027744 -0.007282 0.025274 0.148959
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.496e+00 1.972e-01 17.73 <2e-16 ***
## time_value -1.756e-04 1.086e-05 -16.17 <2e-16 ***
## RestaurantRestrict 1.457e+01 9.167e-01 15.89 <2e-16 ***
## time_value:RestaurantRestrict -7.863e-04 4.976e-05 -15.80 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03994 on 673 degrees of freedom
## Multiple R-squared: 0.5089, Adjusted R-squared: 0.5068
## F-statistic: 232.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073166 -0.027604 -0.007667 0.020993 0.167912
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.837e+00 2.604e-01 18.573 <2e-16 ***
## time_value -2.504e-04 1.437e-05 -17.421 <2e-16 ***
## RestaurantRestrict 6.720e+00 7.322e-01 9.178 <2e-16 ***
## time_value:RestaurantRestrict -3.584e-04 3.977e-05 -9.012 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03856 on 673 degrees of freedom
## Multiple R-squared: 0.4787, Adjusted R-squared: 0.4764
## F-statistic: 206 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065832 -0.025520 -0.007768 0.017189 0.169721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.610e+00 2.408e-01 19.143 < 2e-16 ***
## time_value -2.386e-04 1.329e-05 -17.954 < 2e-16 ***
## RestaurantRestrict 5.397e+00 7.131e-01 7.568 1.25e-13 ***
## time_value:RestaurantRestrict -2.882e-04 3.872e-05 -7.444 2.99e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03638 on 673 degrees of freedom
## Multiple R-squared: 0.4423, Adjusted R-squared: 0.4398
## F-statistic: 177.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061899 -0.022202 -0.005824 0.017238 0.161102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.278e+00 2.117e-01 15.487 < 2e-16 ***
## time_value -1.673e-04 1.168e-05 -14.322 < 2e-16 ***
## RestaurantRestrict 4.011e+00 6.324e-01 6.343 4.13e-10 ***
## time_value:RestaurantRestrict -2.146e-04 3.433e-05 -6.251 7.24e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03208 on 673 degrees of freedom
## Multiple R-squared: 0.3431, Adjusted R-squared: 0.3402
## F-statistic: 117.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067898 -0.026344 -0.005347 0.017755 0.121093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.744e+00 2.423e-01 19.577 < 2e-16 ***
## time_value -2.446e-04 1.337e-05 -18.290 < 2e-16 ***
## RestaurantRestrict 1.989e+00 6.991e-01 2.845 0.00458 **
## time_value:RestaurantRestrict -1.042e-04 3.796e-05 -2.745 0.00622 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03624 on 673 degrees of freedom
## Multiple R-squared: 0.4084, Adjusted R-squared: 0.4057
## F-statistic: 154.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065145 -0.020460 -0.004463 0.016808 0.126407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.889e+00 2.066e-01 18.824 < 2e-16 ***
## time_value -1.991e-04 1.140e-05 -17.461 < 2e-16 ***
## RestaurantRestrict 4.265e+00 5.809e-01 7.342 6.09e-13 ***
## time_value:RestaurantRestrict -2.267e-04 3.155e-05 -7.187 1.76e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03059 on 673 degrees of freedom
## Multiple R-squared: 0.4455, Adjusted R-squared: 0.443
## F-statistic: 180.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109620 -0.025571 -0.004662 0.023084 0.161807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.063e+00 1.591e-01 19.245 < 2e-16 ***
## time_value -1.523e-04 8.734e-06 -17.434 < 2e-16 ***
## RestaurantRestrict 1.532e+01 5.377e+00 2.849 0.00452 **
## time_value:RestaurantRestrict -8.299e-04 2.926e-04 -2.836 0.00471 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04323 on 673 degrees of freedom
## Multiple R-squared: 0.3646, Adjusted R-squared: 0.3617
## F-statistic: 128.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089927 -0.027034 -0.005149 0.019949 0.161567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.737e+00 1.811e-01 20.63 <2e-16 ***
## time_value -1.900e-04 9.968e-06 -19.06 <2e-16 ***
## RestaurantRestrict 1.257e+01 1.237e+00 10.17 <2e-16 ***
## time_value:RestaurantRestrict -6.793e-04 6.709e-05 -10.13 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04059 on 673 degrees of freedom
## Multiple R-squared: 0.4429, Adjusted R-squared: 0.4404
## F-statistic: 178.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.117117 -0.024278 -0.003638 0.021443 0.266197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.688e+00 1.721e-01 15.62 <2e-16 ***
## time_value -1.315e-04 9.468e-06 -13.88 <2e-16 ***
## RestaurantRestrict 1.368e+01 1.315e+00 10.41 <2e-16 ***
## time_value:RestaurantRestrict -7.388e-04 7.140e-05 -10.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04096 on 673 degrees of freedom
## Multiple R-squared: 0.4122, Adjusted R-squared: 0.4096
## F-statistic: 157.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08900 -0.02680 -0.01030 0.02484 0.15150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.921e+00 2.703e-01 18.20 <2e-16 ***
## time_value -2.558e-04 1.492e-05 -17.15 <2e-16 ***
## RestaurantRestrict 1.348e+01 7.536e-01 17.89 <2e-16 ***
## time_value:RestaurantRestrict -7.214e-04 4.093e-05 -17.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03989 on 673 degrees of freedom
## Multiple R-squared: 0.6935, Adjusted R-squared: 0.6921
## F-statistic: 507.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072158 -0.022042 -0.004917 0.018312 0.129049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.962e+00 2.099e-01 23.641 <2e-16 ***
## time_value -2.564e-04 1.158e-05 -22.136 <2e-16 ***
## RestaurantRestrict 1.226e+00 6.003e-01 2.043 0.0415 *
## time_value:RestaurantRestrict -6.109e-05 3.260e-05 -1.874 0.0613 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03128 on 673 degrees of freedom
## Multiple R-squared: 0.4797, Adjusted R-squared: 0.4774
## F-statistic: 206.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067508 -0.020781 -0.004949 0.015887 0.172162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.025e+00 2.084e-01 24.111 <2e-16 ***
## time_value -2.596e-04 1.150e-05 -22.571 <2e-16 ***
## RestaurantRestrict 5.476e+00 6.064e-01 9.030 <2e-16 ***
## time_value:RestaurantRestrict -2.905e-04 3.293e-05 -8.822 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03127 on 673 degrees of freedom
## Multiple R-squared: 0.5819, Adjusted R-squared: 0.5801
## F-statistic: 312.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.107803 -0.026297 -0.008376 0.023005 0.153619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.044e+00 2.476e-01 20.37 <2e-16 ***
## time_value -2.610e-04 1.366e-05 -19.10 <2e-16 ***
## RestaurantRestrict 1.096e+01 6.901e-01 15.88 <2e-16 ***
## time_value:RestaurantRestrict -5.861e-04 3.749e-05 -15.63 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03653 on 673 degrees of freedom
## Multiple R-squared: 0.6641, Adjusted R-squared: 0.6626
## F-statistic: 443.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.090854 -0.024991 -0.007051 0.021346 0.184549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.273e+00 2.371e-01 22.245 <2e-16 ***
## time_value -2.743e-04 1.308e-05 -20.966 <2e-16 ***
## RestaurantRestrict 6.392e+00 6.552e-01 9.756 <2e-16 ***
## time_value:RestaurantRestrict -3.413e-04 3.559e-05 -9.589 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03486 on 673 degrees of freedom
## Multiple R-squared: 0.5386, Adjusted R-squared: 0.5365
## F-statistic: 261.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095004 -0.025269 -0.004117 0.017279 0.157717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.346e+00 1.346e-01 17.437 < 2e-16 ***
## time_value -1.147e-04 7.385e-06 -15.531 < 2e-16 ***
## RestaurantRestrict 3.492e+01 4.754e+00 7.345 5.98e-13 ***
## time_value:RestaurantRestrict -1.896e-03 2.586e-04 -7.331 6.55e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03643 on 673 degrees of freedom
## Multiple R-squared: 0.3638, Adjusted R-squared: 0.361
## F-statistic: 128.3 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079254 -0.022240 -0.005864 0.018478 0.120983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.005e+00 2.143e-01 23.353 < 2e-16 ***
## time_value -2.586e-04 1.183e-05 -21.859 < 2e-16 ***
## RestaurantRestrict 3.693e+00 6.025e-01 6.130 1.50e-09 ***
## time_value:RestaurantRestrict -1.942e-04 3.272e-05 -5.933 4.76e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03173 on 673 degrees of freedom
## Multiple R-squared: 0.5353, Adjusted R-squared: 0.5332
## F-statistic: 258.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077789 -0.024703 -0.007806 0.021095 0.156303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.404e+00 2.389e-01 22.62 <2e-16 ***
## time_value -2.808e-04 1.318e-05 -21.30 <2e-16 ***
## RestaurantRestrict 7.959e+00 6.716e-01 11.85 <2e-16 ***
## time_value:RestaurantRestrict -4.249e-04 3.648e-05 -11.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03537 on 673 degrees of freedom
## Multiple R-squared: 0.5833, Adjusted R-squared: 0.5814
## F-statistic: 314 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083890 -0.024966 -0.007786 0.020549 0.174227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.577e+00 2.415e-01 23.09 <2e-16 ***
## time_value -2.907e-04 1.333e-05 -21.81 <2e-16 ***
## RestaurantRestrict 7.187e+00 6.791e-01 10.58 <2e-16 ***
## time_value:RestaurantRestrict -3.826e-04 3.688e-05 -10.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03576 on 673 degrees of freedom
## Multiple R-squared: 0.5853, Adjusted R-squared: 0.5835
## F-statistic: 316.6 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068183 -0.020108 -0.003325 0.017632 0.128826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.696e+00 1.537e-01 17.540 < 2e-16 ***
## time_value -1.338e-04 8.468e-06 -15.802 < 2e-16 ***
## RestaurantRestrict 5.767e+00 7.266e-01 7.936 8.72e-15 ***
## time_value:RestaurantRestrict -3.103e-04 3.943e-05 -7.869 1.43e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03121 on 673 degrees of freedom
## Multiple R-squared: 0.3664, Adjusted R-squared: 0.3635
## F-statistic: 129.7 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.100199 -0.027587 -0.004691 0.021257 0.208758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.963e+00 1.539e-01 19.253 <2e-16 ***
## time_value -1.469e-04 8.441e-06 -17.400 <2e-16 ***
## RestaurantRestrict 5.310e+01 2.458e+01 2.161 0.0311 *
## time_value:RestaurantRestrict -2.887e-03 1.338e-03 -2.158 0.0313 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04256 on 673 degrees of freedom
## Multiple R-squared: 0.3357, Adjusted R-squared: 0.3327
## F-statistic: 113.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083882 -0.022883 -0.000495 0.018308 0.137664
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.128e+00 1.227e-01 17.349 < 2e-16 ***
## time_value -1.019e-04 6.731e-06 -15.146 < 2e-16 ***
## RestaurantRestrict 2.172e+01 4.457e+00 4.873 1.37e-06 ***
## time_value:RestaurantRestrict -1.178e-03 2.426e-04 -4.856 1.49e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03335 on 673 degrees of freedom
## Multiple R-squared: 0.3858, Adjusted R-squared: 0.3831
## F-statistic: 140.9 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071385 -0.021271 -0.004936 0.016119 0.174623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.182e+00 2.123e-01 19.70 <2e-16 ***
## time_value -2.155e-04 1.172e-05 -18.39 <2e-16 ***
## RestaurantRestrict 6.359e+00 6.178e-01 10.29 <2e-16 ***
## time_value:RestaurantRestrict -3.392e-04 3.355e-05 -10.11 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03186 on 673 degrees of freedom
## Multiple R-squared: 0.5228, Adjusted R-squared: 0.5207
## F-statistic: 245.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064669 -0.025118 -0.009098 0.014791 0.139568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.960e+00 2.358e-01 21.032 < 2e-16 ***
## time_value -2.578e-04 1.301e-05 -19.808 < 2e-16 ***
## RestaurantRestrict 4.727e+00 6.744e-01 7.009 5.85e-12 ***
## time_value:RestaurantRestrict -2.506e-04 3.663e-05 -6.842 1.75e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03515 on 673 degrees of freedom
## Multiple R-squared: 0.4868, Adjusted R-squared: 0.4846
## F-statistic: 212.8 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075308 -0.022470 -0.005361 0.017810 0.199756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.627e+00 2.193e-01 21.098 <2e-16 ***
## time_value -2.392e-04 1.210e-05 -19.764 <2e-16 ***
## RestaurantRestrict 5.560e+00 6.166e-01 9.018 <2e-16 ***
## time_value:RestaurantRestrict -2.948e-04 3.349e-05 -8.804 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03247 on 673 degrees of freedom
## Multiple R-squared: 0.5707, Adjusted R-squared: 0.5688
## F-statistic: 298.2 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095812 -0.025844 -0.003187 0.022885 0.194401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.731e+00 2.547e-01 26.433 < 2e-16 ***
## time_value -3.531e-04 1.406e-05 -25.123 < 2e-16 ***
## RestaurantRestrict 2.351e+00 7.160e-01 3.284 0.00108 **
## time_value:RestaurantRestrict -1.218e-04 3.889e-05 -3.132 0.00181 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0377 on 673 degrees of freedom
## Multiple R-squared: 0.552, Adjusted R-squared: 0.55
## F-statistic: 276.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074054 -0.024533 -0.005863 0.019432 0.182140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.512e+00 2.424e-01 18.61 < 2e-16 ***
## time_value -2.318e-04 1.338e-05 -17.33 < 2e-16 ***
## RestaurantRestrict 5.223e+00 6.757e-01 7.73 3.92e-14 ***
## time_value:RestaurantRestrict -2.763e-04 3.670e-05 -7.53 1.64e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03577 on 673 degrees of freedom
## Multiple R-squared: 0.5304, Adjusted R-squared: 0.5283
## F-statistic: 253.4 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109928 -0.027936 -0.001762 0.025551 0.183727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.666e+00 1.534e-01 17.378 <2e-16 ***
## time_value -1.296e-04 8.418e-06 -15.401 <2e-16 ***
## RestaurantRestrict 4.488e+00 6.035e+00 0.744 0.457
## time_value:RestaurantRestrict -2.389e-04 3.286e-04 -0.727 0.467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04188 on 673 degrees of freedom
## Multiple R-squared: 0.3944, Adjusted R-squared: 0.3917
## F-statistic: 146.1 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079456 -0.024201 -0.005322 0.019249 0.136427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.337e+00 2.250e-01 28.167 < 2e-16 ***
## time_value -3.326e-04 1.242e-05 -26.789 < 2e-16 ***
## RestaurantRestrict 1.911e+00 6.380e-01 2.995 0.00285 **
## time_value:RestaurantRestrict -9.869e-05 3.465e-05 -2.848 0.00453 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03342 on 673 degrees of freedom
## Multiple R-squared: 0.5905, Adjusted R-squared: 0.5887
## F-statistic: 323.5 on 3 and 673 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067598 -0.027399 -0.007896 0.018540 0.163806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.929e+00 2.460e-01 20.038 < 2e-16 ***
## time_value -2.546e-04 1.358e-05 -18.750 < 2e-16 ***
## RestaurantRestrict 3.580e+00 7.036e-01 5.088 4.71e-07 ***
## time_value:RestaurantRestrict -1.906e-04 3.821e-05 -4.990 7.70e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03667 on 673 degrees of freedom
## Multiple R-squared: 0.4634, Adjusted R-squared: 0.461
## F-statistic: 193.7 on 3 and 673 DF, p-value: < 2.2e-16